Intratumor heterogeneity is a common characteristic across diverse cancer types and presents challenges to current standards of treatment. Advancements in high-throughput sequencing and imaging technologies provide opportunities to identify and characterize these aspects of heterogeneity. Notably, transcriptomic profiling at a single-cell resolution enables quantitative measurements of the molecular activity that underlies the phenotypic diversity of cells within a tumor. Such high-dimensional data require computational analysis to extract relevant biological insights about the cell types and states that drive cancer development, pathogenesis, and clinical outcomes. In this review, we highlight emerging themes in the computational analysis of single-cell transcriptomics data and their applications to cancer research. We focus on downstream analytical challenges relevant to cancer research, including how to computationally perform unified analysis across many patients and disease states, distinguish neoplastic from nonneoplastic cells, infer communication with the tumor microenvironment, and delineate tumoral and microenvironmental evolution with trajectory and RNA velocity analysis. We include discussions of challenges and opportunities for future computational methodological advancements necessary to realize the translational potential of single-cell transcriptomic profiling in cancer. By analyzing gene expression patterns in individual tumor cells, researchers can gain patient-specific insights that might inform more effective cancer treatment. Tumors are highly dynamic and heterogeneous collections of cells. Single-cell transcriptomics techniques can offer a valuable window into that complexity but only if the appropriate computational tools are used to analyze the data. Jean Fan of Harvard University, Cambridge, USA, and colleagues have reviewed some of these computational strategies and how they can be employed in cancer research. Single-cell analysis algorithms, for example, can reveal characteristics that distinguish healthy cells from cancerous cells, or indicate how the cells within the tumor may be communicating with each other to promote malignant growth. These are still new technologies, however, and the authors highlight the limitations of the conclusions that can currently be drawn from such analyses. Such heterogeneity presents challenges to current standards of treatment by contributing to metastasis and therapeutic resistance, which ultimately impact clinical outcomes. Accurate characterization of this heterogeneity is essential for delineating the mechanisms of cancer pathogenesis, developing effective treatment strategies, and identifying novel targets for immunotherapy and drug development2.Cancer is a highly heterogeneous disease exhibiting phenotypic diversity driven by molecular aberrations at the genetic, epigenetic, transcriptomic, and protein levels in cells that interact within distinctly spatially organized microenvironments3.The characterization of heterogeneity at the transcriptomic level has been promising, as changes in transcriptional activity and regulation generally underlie cellular phenotypic diversity. The continuous advances in next-generation sequencing technologies such as RNA sequencing (RNA-seq) have enabled the genome-wide quantification of gene-expression levels in a high-throughput manner under diverse conditions. Over the years, these data have led to numerous discoveries in biology, including insights into the phenotypic consequences of molecular aberrations in cancer5.However, such transcriptomic profiling studies have conventionally involved bulk RNA-seq analysis of pooled, heterogeneous mixtures of cells from cancer samples Fig. . Thus, t10 and reviewed14. In terms of computational processing, each particular scRNA-seq protocol, platform, and technology may demand different read processing, quality control, and normalization procedures15. Despite these differences, there are a number of common downstream computational analyses that can be applied  protocols, platforms, and technologies have been developedied Fig. . Here, w16 , or multiple samples from different individuals exhibiting diverse disease states. Such study designs can enable the discovery of transcriptional characteristics shared across patients that may define commonly perturbed molecular pathways in disease. However, the identification of shared cell types or states in data from complex study designs may be difficult when cells are clustered by sample or batch instead of the cell types or states of interest16 Fig. . This ch23. Batch correction is more difficult for scRNA-seq data in which each sample may contain different abundances of cell types, each with distinct gene expression profiles. Therefore, it may not be appropriate to apply the same global adjustment to all cells in a sample. As an alternative approach, many computational methods have been developed for the unified analysis of multiple scRNA-seq datasets that explicitly model or implicitly control for sample and batch-specific differences to identify shared aspects of transcriptional variation across datasets.To facilitate the identification of shared cell types and states across datasets, a number of well-established batch correction methods have been developed for bulk RNA-seq data to remove batch effects by adjusting gene-expression levels to make data from different batches more comparable24 of the normalized gene-expression data to a smaller set of features , aligning these features across datasets, using the aligned features to identify clusters of cells (that may be interpreted as cell types), and finally using the aligned features and identified clusters as the input for 2D visualization algorithms. For example, MultiCCA25 identifies shared aspects of variation between pairs of datasets by iteratively applying canonical correlation analysis24 to two datasets at a time and adding additional samples at each iteration. The canonical components are then adjusted using dynamic time warping and serve as the input for graph-based clustering algorithms and 2D visualization. Mutual nearest-neighbor (MNN) Correct26, Scanorama27, and Conos28 build MNN graphs between cells from different datasets, where two cells are connected in the graph if they are transcriptionally similar. These MNN graphs can then be used directly to derive unified cluster annotation, in the case of Conos, or applied to adjust the data prior to serving as the input for clustering algorithms, in the case of MNN Correct and Scanorama. A major limitation of MultiCCA and MNN Correct is that these methods can produce different results depending on the ordering of the datasets in the analysis27. To overcome the ordering limitation, Scanorama automatically finds a favorable order, while Conos builds a joint MNN graph of all datasets. However, when sample and data acquisition occur continuously and in parallel with the analysis, approaches such as MultiCCA, MNN Correct, and Conos may make it more computationally efficient to incorporate additional datasets into the unified analysis without rerunning all analyses on previously analyzed datasets. In contrast, LIGER29 uses integrative nonnegative matrix factorization30 (NMF) to split the full expression matrix  into two parts: a matrix of shared factors and a matrix of dataset-specific (batch-effect) factors. Then, the shared factors serve as the input for graph-based clustering algorithms and 2D visualization. A notable limitation of all the aforementioned methods is the use of a single categorical variable to encode batch labels. The accommodation of multiple variables could be relevant for cancer data analyses to enable the identification of shared cell types and states across datasets with additional clinically relevant features  or additional dimensions such as time points  or drug dosages. To this end, Harmony31 is able to accommodate multiple categorical variables to encode batch information. Harmony31 iteratively identifies clusters of cells and applies local linear adjustments to these cells while maximizing the diversity of batches within clusters. Such unified analyses with Harmony have been applied to scRNA-seq datasets of hepatocellular carcinoma tumors and immune-relevant sites from multiple patients and platforms to identify T-Regs, exhausted CD8+ T cells, and subtypes of macrophages and DCs that are shared across patients and enriched in cancer samples32.Computational methods for unified single-cell transcriptomics analysis generally search for shared aspects of transcriptional variation that can be aligned across datasets from multiple samples, batches, or conditions Fig. . Despite33 uses Bayesian NMF with prior distributions designed to handle scRNA-seq data to discover latent spaces in a reference scRNA-seq dataset and then uses projectR33 to project new scRNA-seq data onto the learned latent spaces. Such methods thus rely on identified latent spaces being free of batch effects rather than explicitly controlling for batch-specific differences. Such methods may be particularly computationally efficient in the construction of a large reference or atlas model that can then be applied to cells from a smaller dataset under the assumption that the initial reference model contains all possible cell types and states.Alternatively, rather than explicitly taking into consideration batch information, other computational methods for unified single-cell transcriptomics analysis learn a function that maps a dataset onto a low-dimensional latent space and then apply this function to map datasets from different samples or batches onto the same space. For example, scCoGAPS34, their application to datasets from patients with cancer raises additional concerns. The aforementioned computational methods for the unified analysis of scRNA-seq datasets work best when all datasets contain common cell types or states in similar proportions. For example, MultiCCA and MNN Correct assume that all datasets contain at least one shared cell type. However, due to prevalent inter- and intratumoral heterogeneity, this assumption may no longer be valid in a cancer setting. As such, these methods may result in overcorrection when different cell types from different samples are assigned to the same cluster in the unified analysis and misinterpreted as the same cell type. In addition to discrete cell types and cell states, cancer datasets may also contain cells exhibiting smooth developmental and evolutionary trajectories. Unified analysis methods may result in another form of overcorrection when dataset integration fails to preserve the topology of these biological trajectories31. Analyzing each dataset individually using cluster annotations from unified analysis can help to assess the quality of the unified results.While the reliability of many unified single-cell transcriptomic analysis methods has been tested by integrating datasets from many non-diseased tissues36  while embedding the cells in a low-dimensional latent space. The resulting latent space vectors can then serve as the input for clustering algorithms and 2D visualization. In contrast, SAUCIE40 uses a deep neural network in which some of the layers are designed to perform cluster annotation and 2D visualization, thereby eliminating the need for additional clustering analysis of the latent space vectors. Graphical processing units can also be used to fit these deep neural network models more efficiently. While these methods currently use a single categorical variable to encode batch information, they could in theory be extended to allow the inclusion of multiple categorical or continuous variables. However, in contrast to matrix factorization methods such as PCA, CCA, and NMF, in which we can examine the contributions of each gene for each factor, the latent spaces obtained from deep learning methods may not be as easily interpretable. This raises concerns such as overfitting to technical features or other unwanted aspects of variation in the data. Therefore, additional efforts are needed to demonstrate that latent spaces from deep neural networks reflect biologically and clinically relevant patterns in different cancer tissues42.Alternatively, newer approaches for unified single-cell transcriptomics analysis based on deep neural networks have been developed to enable batch correction, normalization, imputation, dimensionality reduction, and clustering for millions of cells simultaneously by fitting a single generative model43 as well as tumor-infiltrating myeloid cells44 in lung cancer to first distinguish different major cell types and subsequently reanalyze each cell type independently to identify finer subtypes and states, including those that are enriched or uniquely present in cancer samples. Such recursive clustering may become more important as the number of cells in new datasets increases. In integrative analyses of cancer datasets from multiple patients, non-neoplastic cells may cluster by cell type, while neoplastic cells segregate by patient45 due to the degree of inter-patient heterogeneity for neoplastic versus nonneoplastic cells. Therefore, when performing such unified analyses across patients, neoplastic cells may need to be considered separately from nonneoplastic cells to identify shared aspects of transcriptional heterogeneity and common cell states. To guard against overcorrection, each sample should be analyzed individually to ensure that the transcriptional programs associated with the states identified from a unified analysis are also present within individual samples. Such integrative analyses using NMF24 have been applied to identify the gene modules that correspond to cell cycle and aberrant developmental programs that mark distinct neoplastic subpopulations and are shared across patients in studies of both diffuse midline gliomas46 and head and neck squamous cell carcinomas47.Once major cell types are identified across datasets, recursive clustering may be applied to identify finer cell states Fig. . RecursiIn the context of cancer, one unique analytical challenge is distinguishing neoplastic cells  from nonneoplastic cells . In some studies, this challenge is circumvented by enriching neoplastic cells and/or depleting nonneoplastic cells by sorting. However, sorting is sometimes impossible due to technical limitations . Furthermore, sorting may be undesirable when the aim is to characterize neoplastic cells in conjunction with nonneoplastic cells in the surrounding tumor microenvironment. Thus, a number of computational methods and approaches have been developed to distinguish neoplastic cells from nonneoplastic cells.3, they may be sufficiently transcriptionally distinct from nonneoplastic cells that they can be segregated through clustering analysis12. To identify transcriptionally distinct cell clusters, a number of computational methods for analyzing individual datasets have been developed52 and reviewed54. Likewise, a number of computational methods for unified analysis across many datasets were described in the previous section. In the context of cancer, these cell clusters may represent different neoplastic or nonneoplastic cell types and states. While such methods can broadly identify cell clusters, annotating these clusters as either neoplastic or nonneoplastic often proves more challenging.As neoplastic cells generally exhibit extensive alterations in a variety of biochemical pathways and oncogenic programs emblematic of cancer+/CD138+ antigen expression, they could be distinguished by the codetection of high CD138 (SDC1) and CD38 gene expression in scRNA-seq data. However, scRNA-seq data may be subject to numerous technical artifacts such as drop-outs, when a gene is expressed but not detected55, or high sparsity, rendering such binary classification based on marker detection liable to false negatives. Furthermore, for other cancers, the detection of marker genes alone is insufficient to distinguish neoplastic and nonneoplastic cells. For example, in a study of pancreatic ductal carcinoma, clustering analysis produced multiple cell clusters identified as ductal cells based on the expression of ductal marker genes56. Without additional information, this clustering analysis alone was unable to determine the malignant status of the identified ductal cell clusters. Although the upregulation of aberrant expression programs such as cancer-associated pathways  may implicate certain cell clusters, annotations based on pathway expression alone may be ambiguous. This is because neoplastic cells can also express genes and pathways typically associated with canonical nonneoplastic cells in ways that we might not expect. For example, an scRNA-seq analysis of glioblastoma identified transcriptionally distinct neoplastic subpopulations exhibiting the upregulation of transcriptional programs associated with expected oncogenic programs such as oncogenic signaling, proliferation, and hypoxia. However, the same analysis identified another subpopulation of neoplastic cells exhibiting the upregulation of complement/immune response programs typically associated with immune cells57. As such, orthogonal evidence beyond marker gene or pathway expression is often needed to confidently distinguish between neoplastic and nonneoplastic cells.In certain cancers, the detection of distinct marker genes or combinations of marker genes can distinguish neoplastic from nonneoplastic cells Fig. . For exa58. The presence of deletions or amplifications will on average lead to reduced or increased expression of genes, respectively, within affected loci compared to copy-neutral reference expression for the same cell type. Hierarchical clustering of smoothed normalized expression magnitude deviations can distinguish cells harboring CNVs from normal diploid cells. In a study of pancreatic ductal carcinoma, such expression-based CNV inference was used to show that one ductal cell cluster exhibited higher CNV levels than another ductal cell cluster; in combination with the upregulation of aberrant cancer-related programs such as cell proliferation, migration, and hypoxia, these findings implicated the former ductal cell cluster as the malignant subpopulation56. Overall, CNV inference from scRNA-seq data has been applied to distinguish neoplastic and nonneoplastic cells in many cancers, including a variety of gliomas61, melanoma45, head and neck cancer47, breast cancer62, and multiple myeloma58.To this end, computational methods have been developed to identify DNA-level aberrations directly from scRNA-seq data Fig. . Large-s58. An appropriate normal reference is needed to ensure that the observed deviations in expression magnitude are the result of underlying copy number changes rather than platform- or cell-type-specific differences. The identification of an appropriate normal reference may be particularly challenging if the cancer cell type of origin is unknown. An alternative computational approach for identifying CNVs is based on the variant allele frequencies (VAFs) of heterozygous germline single-nucleotide polymorphisms (SNPs)58. Although most scRNA-seq studies focus on gene-expression counts, scRNA-seq also provides information about SNPs by virtue of sequencing-based data. Changes in copy number skew the observed VAFs12 in scRNA-seq data such that the presence of deletions leads to the persistent depletion of the lost allele, while amplification will lead to increased abundance of the amplified allele on average. Since allele-based approaches rely on high coverage of many SNP sites, data from scRNA-seq protocols that can achieve full-transcript coverage  are best for these analyses. In contrast, the analysis of data from high-throughput scRNA-seq protocols that capture only the 3′ or 5′ transcript ends will be limited to the identification of larger whole-chromosome and chromosome-arm-scale alterations. Furthermore, allele-based approaches will not be able to confidently distinguish copy-neutral loss-of-heterozygosity from deletions or to distinguish different numbers of copy-number amplifications. Integrating allelic and expression information can overcome these limitations and achieve more robust probabilistic CNV inference58.However, the reliability of such expression-based CNV inference is dependent on how well the cancer expression profile is matched to the normal reference, in terms of both technical and biological factors63, prevalent stochastic mono-allelic expression and detection65, and allelic exclusion66) limit our ability to confidently call heterozygous point mutations using scRNA-seq alone67. As such, alternative protocols and technologies have been developed to combine scRNA-seq with targeted locus-specific amplification69 or targeted quantitative polymerase chain reaction-based mutation detection67 to enable the robust detection of selected point mutation status directly from or in conjunction with scRNA-seq data. Furthermore, in the calling of somatic point mutations from scRNA-seq data, false positives that may be caused by RNA editing must also be considered. The reduction of false positives may be achieved by limiting the analysis to the mutations and variants identified through the WES of the same tumor sample or to mutations known to be recurrent in relevant cancers from databases such as COSMIC70. Some computational approaches have also been specifically designed to call point mutations from RNA-seq71 and scRNA-seq data73 while taking into consideration such potential false positives and negatives.Nevertheless, some cancers do not harbor such large-scale CNVs. Other smaller-scale DNA-level alterations such as somatic point mutations can also be identified from scRNA-seq data and used to distinguish neoplastic cells Fig. . However75, highlighting the need for the further assessment of the interplay of genetic and transcriptional heterogeneity.Beyond distinguishing neoplastic cells from nonneoplastic cells, CNV inference and somatic mutation calling can be used to distinguish genetically distinct neoplastic subclones. Notably, by inferring such alterations from scRNA-seq data, the transcriptional profiles of genetic subclones can be directly compared to characterize the transcriptional consequences of observed genetic alterations Fig. . NeverthBCR-ABL fusion gene. While gene fusions may be detected in data generated with full-transcript scRNA-seq protocols , limitations in detection sensitivity can result in false negatives76. To robustly detect gene fusions, the scRNA-seq library preparation protocol can be modified to include primers for the targeted amplification of specific gene fusions. One study of CML successfully applied this approach with a primer targeting the BCR-ABL fusion gene to confidently distinguish CML cells76. Ultimately, a combination of these approaches should be used and can even be integrated with machine-learning classifiers69 to identify bona fide neoplastic cells.Nevertheless, some cancers are not well defined by either large-scale CNVs or somatic point mutations. For example, chronic myeloid leukemia (CML) cells are generally defined by the presence of the 77, angiogenesis78, and therapeutic resistance79. scRNA-seq provides the opportunity to characterize the many cell types in the tumor microenvironment, from stromal fibroblasts to diverse immune subtypes, in a high-throughput and mostly unbiased manner. In a study of the lung tumor microenvironment, scRNA-seq analysis identified functionally distinct fibroblast subtypes as well as remodeled tumor endothelial cells that downregulate antigen presentation and contribute to immune tolerance and suppression43. Similarly, the scRNA-seq analysis of tumor-associated macrophages has been used to distinguish between proinflammatory macrophages that resist tumor progression and tissue-reparative macrophages that promote tumor growth and metastasis in breast cancer80, lung cancer81, and hepatocellular carcinoma32. Furthermore, the scRNA-seq analysis of tumor-infiltrating T cells has identified notable subpopulations in different cancers that may be potential targets for novel immune checkpoint inhibitors, including exhausted T cells in lung cancer43, tissue-resident memory T cells in breast cancer82 and melanoma83, and regulatory T cells in non-small-cell lung cancer84 and colorectal cancer85.Neoplastic cells exist among the heterogeneous composition of nonneoplastic cell types and states within a tumor microenvironment that may contribute to tumor evasion and progression88 using a curated list of known receptors and corresponding ligands89  and estimated cell-type proportions for all samples (unobserved) using different types of regression models, such as linear regression93 or support vector regression95. Different approaches for cell-type marker gene selection approaches can influence the accuracy of cell-type proportion estimates. Marker genes can be selected in many ways, including differential expression analysis or the use of the Gini index96. Likewise, methods can weight genes by variability97 or incorporate patient-specific covariance98 to improve marker selection. Accurate proportion estimation for transcriptionally similar cell types is particularly challenging. To address the collinearity of gene expression profiles for similar cell types, one approach is to remove the union of the most highly expressed genes among similar cell types from marker selection93. Another approach is to first estimate the proportion of a group of transcriptionally similar cell types and then recursively select markers and obtain estimates for finer cell types within each group98. Such deconvolution approaches have been applied to estimate the proportion of infiltrating immune cells for 23 cancer types from The Cancer Genome Atlas (TCGA), in which increased immune infiltration was found to be associated with longer median survival93. An important caveat to consider in any deconvolution analysis is that cancer cells may aberrantly express genes associated with canonical immune or nonneoplastic cell types. Therefore, to achieve accurate proportion estimates, the incorporation of neoplastic cells in marker gene selection is necessary to help ensure that the detection of marker genes reflects the underlying proportions of immune cells rather than aberrant expression by neoplastic cells.Approaches focused on scRNA-seq datasets alone can be limited in terms of their statistical power due to the limited number of patients and samples profiled. To take advantage of the greater availability of large collections of bulk RNA-seq samples, computational deconvolution approaches have been developed to infer the proportions of different immune and stromal cell bulk RNA-seq samples after the identification of cell-type-specific markers from scRNA-seq data101, compared102, and reviewed103. In the context of cancer, trajectory inference analysis has been applied to scRNA-seq data from healthy and cancerous kidneys. The analysis identified two divergent transcriptional trajectories, one of which corresponds to the development of nephrogenic rest cells and the other to Wilms cancer cells originating from cells of the ureteric bud, consistent with the hypothesis that Wilms tumor cells develop from aberrations in fetal nephrogenesis104. Likewise, trajectory inference analyses of infiltrating T cells in liver cancer105 and small-cell lung cancer84 have identified cellular state transitions between proliferating/activated and exhausted states.While single-cell transcriptomic profiling techniques such as scRNA-seq offer transcriptome-wide molecular measurements at a single-cell resolution, these measurements ultimately represent a single snapshot in time. This lack of temporal information is particularly limiting for the study of cancer and other dynamic processes due to the continuous nature of cancer evolution and, more broadly, cellular development. Although scRNA-seq provides a snapshot of each individual cell at a single point in time, a snapshot of many cells representing a range of evolutionary stages can allow us to order these cells in pseudotime and within trajectories. To infer this pseudotime ordering of cells within putative trajectories, a number of computational trajectory inference methods have been developed106  and exonic  reads in scRNA-seq data to infer the rate of change in transcript abundance to estimate the future transcriptional state of a cell106 Fig. . The obs106 Fig. . For exa107. This problem may be particularly pronounced in cancer, where scRNA-seq may capture multiple transcriptionally distinct subpopulations of cells but not the ancestral cells that gave rise to these populations74. Despite these challenges, the application of RNA velocity analysis to isocitrate dehydrogenase (IDH) wild-type glioblastoma cells has putatively identified an intermediate glioma stem-like cell (GSC) subpopulation that may transition from a mesenchymal to a proneural phenotype, implicating mesenchymal GSCs as the progenitors of proneural GSCs in IDH wild-type glioblastomas108. Nevertheless, RNA velocity analysis assumes that increased relative intronic expression reflects the presence of unspliced nascent transcripts. In a cancer setting, however, mutations in the splicing machinery may cause aberrant alternative splicing, resulting in differentially regulated intronic retention that violates this assumption. For example, in chronic lymphocytic leukemia as well as other myeloid neoplasms, recurrent mutations in splicing factor genes such as SF3B1, U2AF1, SRSF2, and ZRSR2 have been observed109 and shown to cause a wide variety of aberrant alternative splice variants111. In such a scenario, RNA velocity models should explicitly avoid introns known to be impacted by aberrant splicing by excluding these introns from unspliced gene quantification or removing these genes from the model altogether.While the application of trajectory inference and RNA velocity analysis offers the potential to identify the altered mechanisms of cell development in cancer pathogenesis, a number of precautions should be considered when applying such analyses in a cancer setting, especially when interpreting results for neoplastic cells. Trajectory inference relies on the adequate representation of cells in different developmental stages, such that the absence of intermediate stages may distort the inferred temporal dynamicsThe application of single-cell transcriptomics in cancer presents a number of unique analytical challenges and opportunities. In this review, we focused on emerging themes in the computational analysis of single-cell transcriptomics data in cancer research, highlighting unique challenges and opportunities.3. Even within poly-A selection scRNA-seq protocols, droplet-based protocols that are restricted to only 3′ or 5′ ends will inherently be more limiting for allele-based CNV inference, mutation calling, and fusion gene detection compared to full-transcript single-cell RNA-seq protocols. Furthermore, some cell types  may not be compatible with the dissociation, encapsulation, or other processing steps of all scRNA-seq protocols. Alternative protocols such as single-nucleus RNA-seq112 (snRNA-seq) may be applied to these cell types and to cells from frozen specimens. Different sample preservation techniques, such as the freezing or formalin fixed-paraffin embedding of tissues commonly used for cancer samples, may require different protocols and introduce different limitations113. Future unified analyses of the same cancer samples with protocols  will help to further elucidate the precise limitations and biases introduced by each protocol. As always, care must be exercised in selecting the protocol that is best able to address the question of interest while remaining aware of its inherent limitations, to avoid drawing spurious biological conclusions.However, despite the promise of single-cell transcriptomics and opportunities for computational method development in cancer research, discoveries will always be fundamentally limited by what can be measured. Notably, although a variety of protocols exist for scRNA-seq analysis, nearly all involve poly-A selection, thereby limiting the ability to examine non-polyadenylated transcripts, such as small nucleolar RNAs, histone mRNAs, pre-mRNAs, and long noncoding RNAs, which may play diverse regulatory roles in cancer114 have been developed to enable unified analysis across transcriptomic and epigenetic data modalities, albeit for different cells. However, these approaches generally rely on linking functions to perform mapping between data modalities, such as mapping between gene expression and promoter or gene body accessibility under the assumption that greater accessibility correlates with a higher gene expression magnitude. In a cancer setting, however, these assumptions may no longer be valid when regulatory factors are mutated. As such, greater consideration may be necessary to ensure that linking functions are appropriate when applying such computational data integration approaches in a cancer setting. New technologies and protocols are also being developed to enable multimodal measurements, including simultaneous transcriptomic and epigenomic measurements, within a single cell116. In conjunction with novel computational analysis approaches, such technologies offer the potential to contribute to our growing understanding of these different aspects of cancer heterogeneity and their interplay. Likewise, to investigate the role of the spatial context, a number of imaging a number of imaging technologies have been developed technologies have been developed to enable the targeted spatially resolved single-cell transcriptomic profiling of 100–1000 s of genes119. More recently, these technologies have been expanded to a near-genome-wide scale122. We anticipate that such spatially resolved single-cell transcriptomic data generated from these different technologies will require new computational pipelines and methods for proper processing , quality control, and normalization. Furthermore, the computational methods for analyzing scRNA-seq data may need to be modified to develop in situ analogs appropriate for spatially resolved single-cell transcriptomics data. RNA velocity analysis of scRNA-seq data that leverages the ratio of intronic and exonic gene expression has been modified to create an in situ analog that leverages the ratio of nuclear and cytoplasmic gene expression120. In conjunction with novel computational analysis approaches, these emerging technologies offer the potential to contribute to our growing understanding of these different aspects of cancer heterogeneity and their interplay.Although transcriptional heterogeneity has been observed in a variety of cancers, the extent to which this transcriptional heterogeneity can be mapped to underlying genetic, epigenetic, or spatial contextual causes and their interplay remains unclear. While the transcriptional impact of genetic variants may be assessed to some degree through the direct inference of genetic information from scRNA-seq data, as previously discussed, the identification of associations with other aspects of heterogeneity such as epigenetic or spatial heterogeneity may require the integration of additional data, technologies, and computational methods. For example, to investigate the role of epigenetic heterogeneity and its interplay with transcriptional heterogeneity at the single-cell level, a number of multiomic computational analysis approaches123, Human Developmental Cell Atlas, Pediatric Cell Atlas124, HuBMAP125, Human Tumor Atlas Network, LifeTime EU Flagship, and others, there is a need to improve the scalability of computational methods through implementation improvement and algorithmic optimization. While the application of these computational methods to a cancer setting may present a number of unique challenges, additional efforts are ultimately needed to iterate between data-driven hypothesis generation and the orthogonal validation of computational predictions. Despite these challenges, single-cell transcriptomics analysis presents tremendous opportunities to contribute to our understanding of cancer heterogeneity, pathogenesis, evolution, and microenvironmental interactions to lay a foundation for new therapeutic innovations.As the numbers of analyzed cells and samples continue to increase exponentially, particularly because of international and collaborative efforts such as the Human Cell Atlas


Perceived social support represents an important predictor of healthy aging. The global COVID-19 pandemic has dramatically changed the face of social relationships and revealed elderly to be particularly vulnerable to the effects of social isolation. Social distancing may represent a double-edged sword for older adults, protecting them against COVID-19 infection while also sacrificing personal interaction and attention at a critical time. Here, we consider the moderating role of social relationships as a potential influence on stress resilience, allostatic load, and vulnerability to infection and adverse health outcomes in the elderly population. Understanding the mechanisms how social support enhances resilience to stress and promotes mental and physical health into old age will enable new preventive strategies. Targeted social interventions may provide effective relief from the impact of COVID-19-related isolation and loneliness. In this regard, a pandemic may also offer a window of opportunity for raising awareness and mobilizing resources for new strategies that help build resilience in our aging population and future generations. The novel coronavirus disease 2019, SARS-CoV-2 or COVID-19, is now recognized as one of the most contagious human viral infections. It gave rise to a global pandemic and a global health emergency with dramatic, still unpredictable social and economic long-term consequences for our society. Elderly individuals seem to be particularly vulnerable to developing severe COVID-19 infection, with higher risk of morbidity and mortality than any other age group , 4. Nevehttps://www.who.int).The past century has seen an extension in life expectancy globally, almost doubling the average lifespan in Western countries . The gloAcceleration in the biological processes of cellular aging, experiential, environmental and pathological factors may raise individual vulnerability to COVID-19 infection and severe symptoms. This is especially true for elderly individuals with chronic medical conditions like diabetes as they are reportedly at higher risk of developing severe COVID-19 complications , 12. BasThe intensity of social relationships in Western societies is generally diminishing, and elderly are particularly concerned by social isolation and feelings of loneliness , 37. AltThe nature of social relationships dramatically changes as individuals age, however. Social networks tend to narrow with increasing age, and novelty seeking, which facilitates building new social relationships, becomes reduced in old age . Hence, The role of social experiences in health outcomes demands particular consideration with respect to the COVID-19 pandemic. During the COVID-19 pandemic, physical distancing was introduced as an effective prevetive measure to reduce the risk of infection . While tHere, we will consider the moderating role of social relationships as a potential influence on COVID-19 vulnerability and adverse outcomes in the elderly population. The presented literature will elucidate the hypothesis that resilience of the host to SARS-CoV-2 infection and associated complications will be reduced by cumulative lifetime stress exposure. Considering that lifetime exposure to various stresses may represent the first “hit”, social isolation in older age may exacerbate these effects as a second “hit” and generate a potentially harmful stress burden. We will discuss the underlying physiological mechanisms of psychosocial stress and integrate the existing literature to propose a unifying concept of the social determinants of healthy aging and potential mitigation strategies.interact with others across the lifespan, and at the same time also influence others while accepting influences from them. This reciprocal influence of social life brings about changes at different levels  [Humans did not evolve to be alone . Influenaviours) . The humaviours) , and evoaviours) , reflectaviours) . Social aviours) . Both coaviours) .per se does not necessarily guarantee health promotion. Investigations are mainly focusing on understanding and characterizing the dynamics underlying positive social interaction whereas its negative face is largely ignored, particularly by mainstream gerontological research. Negative interactions may entail unpleasant social encounters that are characterized by the lack of reciprocity, criticism and rejection [Psychological processes that contribute to healthy aging via social interactions appear to exert protective functions against maladaptive physical disease states , 41. Altejection , or discejection . Althougejection , arguablPositive interactions between individuals in a social network  may imprThe nature of social relationships changes during the lifespan. Aging in particular may be accompanied by social disengagement or withdrawal  driven bInterestingly, it appears that males and females, both human and animal, respond differently to social interactions . These dESR1 and ESR2) signals may regulate social recognition in women [In line with what has previously been established in female rodents , estrogein women . Androgein women . For exain women . In addiA general assumption is that there are functional perturbations with advancing age that result in dysfunctional activity of various endocrine glands and their target organs , 117. ThThe activation of the HPA axis and elevated circulating GCs are essential for adaptive responses to acute and chronic stress and thus elemental for the individual’s survival, even though its dysregulation is apparent during aging. Because complications in HPA axis activity in the elderly begin with high GC levels, experimental challenges have highlighted the impaired GC-mediated inhibitory (negative) feedback loop and reduced GC receptor sensitivity in the brain . Both coAlthough this topic falls beyond the scope of the present review, it is necessary to state that prolonged disturbances in HPA axis function are closely tied to psychophysiological pathologies in elderly, such as higher risk of cognitive decline -140, depHealth-related concerns linked to an overactive HPA axis also emerge from the sexual dimorphism observed in humans  and non-It has been widely recognized that HPA axis activity can be regulated by a socially stimulating environment , 160-162Socially engaging experiences also appear to promote healthy aging via OT and brain-derived neurotrophic factor (BDNF) regulation. In fact, the benefit of social support was causally linked to elevated OT levels, but TL became elongated only in females. Interestingly, the OT antagonist L-366,509 blocked all benefits of social housing , 102. ItOlder adults are at higher risk for COVID-19 complications than the younger population, partially due to a weaker immune system and potentially other pre-existing health conditions . Aging iThe immune system, which acts in close dialogue with the neuroendocrine system including the HPA axis , also exa) in the innate immune system [The molecular and cellular foundations of immunosenescence are still unknown. A growing body of evidence, however, has attributed the age-related immune decline to overproduction of pro-inflammatory cytokines such as interleukin 1β (IL-1β), interleukin 6 (IL-6), interleukin 18 (IL-18), C-reactive protein (CRP), and tumour necrosis factor alpha . The period of physical distancing during COVID-19 may have therefore exposed the most vulnerable individuals and populations and revealed where social support and interventions are most urgently required.In spring 2020, health agencies and governments around the globe have recommended lockdown and physical distancing in order to contain the spread of COVID-19. These measures have been widely successful in epidemic control by limiting the exposure to the general population and reduced by 45%) COVID-19 transmission and associated long-term health risks  [Elderly people are among the most vulnerable individuals of our society. As people age, they become increasingly susceptible to adverse consequences of social-physical distancing and withdrawal. This may lead to gradual reduction in elderly individuals interacting with their social environment, thus shrinking their social network. Also, a growing global gender gap in the proportion of elders (over 60) who reside alone (17% of women vs. 9% of men) has been reported . In the acl.gov) . Yet, byacl.gov) , 211-213acl.gov) . In expeacl.gov) . In humaacl.gov) . These fSocial isolation is typically referred to an objective state of reduced social contact and networking  or disenThe concept of social isolation and loneliness portrays a profile of profound impact on mortality in older adults, either synergistically , 223 or Aside from correlational investigations linking social exposure to psychophysiological health in later life , 230, a Even if health professionals and policy makers consistently follow up with the above concerns, there are also further complications that need to be seriously addressed. For one, social isolation and loneliness in later life represent a geo-specific dynamic impact on health and regional interplay, most likely due to the influence of sociocultural determinants. While these two are not highly correlated in the USA and UK , 225, anIn addition to cultural measures, the individual’s past experiences and even that from previous generations may generate complex interaction and synergies with the biological aging process and social life , 25, 242etc.) [Stress, in this review, refers to endocrine and behavioural responses to stressors that perturb homeostasis in a number of physiological systems  , 247. Thetc.) , 247. AlAllostatic load (AL), in contrast, reflects the body’s “wear and tear” when the mediators of allostasis fail to promote resilience but rather accumulate the biological burden associated with repeated stressful hits . NotablyCumulative wear and tear by recurrent stresses may raise the risk of age-related physiological complications and the susceptibility to adverse health outcomes . Loss ofThe COVID-19 pandemic and the required social contact restriction for epidemic control have challenged daily routines of social life in the elderly population, such as restricted contact with family members, friends and even care providers for an extended period of time. Future research will provide evidence if the design and development of targeted social interventions may provide effective relief from the impact of COVID-19-related social isolation and loneliness. Previously designed social interventions for older adults showed that they can be effective when provided as one-on-one assistance . Such inExperimental studies have also been able to specifically study the impact of social interactions on long-term health and aging. For example, environmental enrichment (EE) by housing laboratory rodents in social groups has become a widely used paradigm that effectively reduces the chronic burden of stress and promotes long-term health and aging . This reThe global COVID-19 outbreak in 2020 has fundamentally changed ways of social interaction and communication at all levels and at all ages. The impact is being particularly felt in the aging population and has called for intergenerational solidarity. Staying socially engaged is a critical part of successful aging, and staying connected is a vital part in combating loneliness, anxiety and depression , 230. Ma


Cell Host and Microbe, Fiege and colleaguesIn a recent issue of  Cell Host and Microbe, Fiege and colleagues1 report that laboratory mice exposed to pathogens from pet-store mice exhibit impaired humoral immunity to influenza vaccination and display gene expression signatures that more authentically reflect human vaccine responses.In a recent issue of  The use of laboratory mice to evaluate infection and to measure the immunogenicity, efficacy, or mode of action of vaccines has been a crucial tool in medical research. Studies in mice have advanced our understanding of how specific genes contribute to disease and have enabled the identification of effectors involved in antigen presentation and the development of protective immunity. This fundamental knowledge has underpinned the development of vaccines, therapeutics, and licensed drugs.Specific-pathogen-free (SPF) mice are free from a defined list of viruses, bacteria, or parasites. These animals are housed in clean conditions that minimize exposure to pathogens which could introduce uncontrolled variables into experiments. There are many advantages to using laboratory mice: they are small and easy to house, extensive reagents are available, diverse genetic backgrounds exist, and we have the capability to engineer genetically modified transgenic strains and humanized mice or produce bone marrow chimeric mice. Collectively, these factors allow rigorous, detailed investigations into mechanisms underlying disease, correlates of protection, or responsiveness to vaccination.,,,Despite the utility of mice in pre-clinical studies, they often fail to recapitulate hallmarks of human disease, and findings in mice do not always translate into humans. Diverse factors contribute to this, including genetic and physiological differences, species-specific characteristics of pathogens, or sex differences. Recently, consideration has been given to the role of environmental factors in modulating host immune outcomes following infection and/or immunization, including diet/nutritional status,,+ T cells were observed in co-housed “dirty” mice.There is growing evidence that exposure to pathogens can mature the murine immune system, resulting in an antigen-experienced state that may more accurately reflect immunological maturity in humans.Gene expression profiles in cells from dirty or SPF mice prior to and 3 days post-immunization with an inactivated influenza vaccine (IIV) were measured. Drawing from existing transcriptomic datasets from humans immunized with similar IIVs at the same time-points, the authors compared post-immunization gene signatures in both species. They determined that vaccine-responsive genes in dirty mice more accurately mirrored responses in humans. Therefore, pre-clinical vaccine studies performed in dirty mice might be a more relevant predictor for vaccine responses in humans. To interrogate this in more detail, mice were challenged with influenza H1N1 strain A/Puerto Rico/8/1934 (PR8) or pandemic H1N1 strain A/California/04/2009. Primary responses to infection were similar, suggesting that immune-experience did not appear to affect the pathogenesis of wildtype influenza infection. However, when the authors immunized dirty and SPF mice with live attenuated influenza vaccine (LAIV), split-virion IIV, and adjuvanted IIV platforms, they found that dirty mice displayed impaired induction of humoral immunity with reduced titers of neutralizing antibodies (NAbs), reduced binding of virus-specific IgG subclasses, and a limited capacity to control viral replication in the lung.+ T cells were detected in the lung parenchyma of dirty mice prior to challenge. The implications of this were not defined, but will undoubtedly be the subject of future work. In conclusion, the authors proposed that SPF mice mount exaggerated responses to immunization that result in superior protection from challenge. Considering that the majority of next-generation influenza vaccines are initially evaluated in SPF mice, exclusive use of these mice may limit subsequent translational relevance, particularly in light of the authors’ findings that vaccine responsive gene expression in dirty mice mimics adult humans.The development of universal influenza virus vaccines that elicit cellular immunity against conserved internal antigens such as nucleoprotein (NP) is underway.The factors that affect the efficacy of influenza vaccines are complex, and a single mouse model cannot address all hypotheses. Immunogenicity or efficacy depends on the antigen or epitope target, the vaccine platform or formulation, the route of administration, the phenotype of immunity, as well as prior exposure to influenza viruses. Indeed, the extensive history of exposure to influenza viruses in humans is a major confounding factor when translating pre-clinical studies into humans. Nonetheless, the findings described by Fiege and colleagues


Musculoskeletal deficits are among the most commonly reported extra-intestinal manifestations and complications of inflammatory bowel disease (IBD), especially in those with Crohn’s disease. The adverse effects of IBD on bone and muscle are multifactorial, including the direct effects of underlying inflammatory disease processes, nutritional deficits, and therapeutic effects. These factors also indirectly impact bone and muscle by interfering with regulatory pathways. Resultantly, individuals with IBD are at increased risk of osteoporosis and sarcopenia and associated musculoskeletal morbidity. In paediatric IBD, these factors may contribute to suboptimal bone and muscle accrual. This review evaluates the main pathogenic factors associated with musculoskeletal deficits in children and adults with IBD and summarises the current literature and understanding of the musculoskeletal phenotype in these patients. Musculoskeletal deficits are among the most common extra-intestinal manifestations and complications of inflammatory bowel disease (IBD), especially in those with Crohn’s disease (CD). Specifically, patients with IBD are at an increased risk of osteoporosis, a systemic disorder characterised by low bone mass and deterioration of bone microarchitecture, with consequent increased risk of fractures throughout their lifetime . In adulOn top of the well-established bone defects, emerging studies in the paediatric and adult IBD show that muscle mass and function are low compared to healthy peers ,6. ThesePro-inflammatory cytokines perpetuate gastrointestinal inflammation and tissue destruction in IBD while regulating extra-intestinal manifestations of the disease, including bone and muscle deficits. The role of inflammation in the pathogenesis of skeletal deficits in IBD has been highlighted by reduced bone mass in newly diagnosed cohorts of treatment naïve paediatric IBD patients ,13,14 anSeveral cytokines are known to be chronically elevated in the serum of individuals with IBD, such as TNF-α, IL-1β, and IL-6 inhibit osteoblast function (responsible for bone formation) and promote osteoclastogenesis (responsible for bone resorption) ,17,18. TTNF-α and IL-6 are also key regulators of muscle loss in CD, a pathway shared with aging. Circulating TNF-α and IL-6 are increased in healthy elderly adults and are negatively associated with muscle mass and function . These aIndividuals with IBD are at increased risk of sub-optimal nutritional status which can itself impact bone and muscle. The aetiology of poor nutritional status in IBD is multifactorial and includes disease activity associated with anorexia, exclusion diets, medication-induced nausea, pain from strictures, and malabsorption from both active disease and small bowel resections. However, both undernutrition and overnutrition are known to be associated with adverse effects on the musculoskeletal system. Historically, rapid and unsolicited weight loss was a presenting symptom of disease in both CD and ulcerative colitis (UC), indicative of nutritional deficiency. For example, malnutrition as defined by a body mass index (BMI) Z-score < −2 SDS was observed in approximately 30% of paediatric CD patients at diagnosis in one prospective study, reducing to 15% at the long-term follow-up . Some hiDietary intake and composition are intrinsically linked to disease management in IBD, as exemplified by the efficacy of exclusive enteral nutrition (EEN) in inducing remission and mucosal healing in CD ,39 and oInadequate dietary intake has been reported in patients with both active and inactive IBD ,48,49,50Contrary to the typical undernutrition observed in IBD, clinicians managing these patients are encountering an increasing frequency of patients who are overweight and obese, in line with the obesity epidemic present in the general population. Prolonged systemic glucocorticoid therapy by itself could of course contribute to weight gain. This creates a separate issue of overnutrition that may contribute to bone and muscle defects. Recent studies show that the prevalence of obesity among adults with IBD is estimated at 10–50% ,52,53, wBMI is generally used as a clinical marker of nutritional status. However, it is an insensitive measure of adiposity as abnormal body composition, characterised by low lean mass and maintained or increased fat mass, is common in paediatric and adult IBD . ResultaSystemic glucocorticoids are commonly administered as first-line induction therapy for moderate to severe IBD in adult and paediatric patients. Glucocorticoids are effective in achieving clinical remission in IBD; however, they are associated with systemic toxicity, including profound and devastating effects on bone and muscle. Chronic glucocorticoid exposure can lead to muscle atrophy and glucocorticoid-induced osteoporosis with an increased risk of fragility fractures ,62,63. IIn adults, glucocorticoid-induced bone and muscle loss occur in a rapid, dose-dependent manner. Glucocorticoids induce preferential loss of metabolically active trabecular bone , highligIn children, glucocorticoids directly inhibit linear growth via peripheral and central mechanisms which in turn can impair bone development. Glucocorticoids cause local insensitivity to growth hormone (GH) and insulin-like growth factor 1 (IGF-1) at the growth plate by disrupting GH/IGF-1 receptors in chondrocytes . GlucocoThe catabolic effects of glucocorticoids within the muscle may also negatively influence bone. The muscle wasting properties of glucocorticoids can be observed as little as seven days after initiation of therapy . GlucocoAll the above has led to cautious and limited use of glucocorticoids in IBD and early institution of steroid-sparing measures. Indeed, avoidance of repeated courses of glucocorticoids is a key performance indicator in the audit of UK IBD units . While tVitamin D has pleiotropic physiological functions but is primarily recognised for its role in the regulation of calcium homeostasis and bone metabolism. Low levels of vitamin D can lead to secondary hyperparathyroidism, whereby excessive parathyroid hormone expression accelerates bone turnover with subsequent reductions in bone strength. Additionally, vitamin D plays an integral role in both innate and adaptive immunity (extensively reviewed elsewhere ), includWhile there is growing interest in the link between vitamin D and skeletal muscle mass and function, a direct causal role for low vitamin D in IBD-associated musculoskeletal pathology has not been clearly established. To date, no studies have investigated the direct role of vitamin D status on skeletal muscle of patients with IBD. However, low serum vitamin D3 was associated with impaired activation of muscle protein synthesis pathways in the skeletal muscle of young adults with CD . AdditioChronic inflammatory diseases such as IBD can interfere with the systemic GH/IGF-1 axis. Inflammatory cytokines, glucocorticoid exposure, and poor nutrition all adversely affect GH/IGF-1 with subsequent impacts on health outcomes. The GH/IGF-1 axis is a primary regulator of linear skeletal growth and muscle accrual during childhood and puberty . The GH/In paediatric CD, faltering linear growth is a concern that often precedes gastrointestinal symptoms and may be linked to a range of systemic abnormalities in the GH/IGF-1 axis, including a relative state of hormone insensitivity or hormone insufficiency . SystemiGenerally, normal levels of GH secretion with reduced IGF-1 and IGF binding proteins (IGFBPs) in paediatric CD have been reported in published studies, which reflects a state of systemic GH insensitivity . HoweverSex steroids are essential for bone and muscle accrual in adolescents and maintenance of these outcomes in adults. Puberty is a critical period of bone and muscle growth that is moderated by the associated surge in sex steroid production, accompanied by an increase in the amount and amplitude of GH. Bone mass approximately doubles between the pre-pubertal and young adult age, and 90% of peak bone mass will be attained by late adolescence. Oestrogen is essential for the maturation and mineralisation of bone and the closure of epiphyseal growth plates in both sexes , and oesThe insidious onset of paediatric IBD typically manifests in the pre or early pubertal age and can delay the timing of puberty. Delayed puberty is more common among children with CD and less so in those with UC. Adolescents with CD who fail to achieve disease remission or who experience frequent disease relapses are at the greatest risk of pubertal delay. Studies have used various maturational markers to assess pubertal delay in paediatric CD, including bone age , age at Similarly, nutritional deficits, inflammatory cytokines, and prolonged use of glucocorticoids are implicated in the pathogenesis of pubertal delay in adolescents with CD . ChronicFew studies have been conducted to assess sex steroid deficiency in adults with IBD. Serum testosterone has been reported lower in men with CD compared to healthy controls, but clinically relevant testosterone deficiency was uncommon ,117. LowMuscle and bone health are intricately related across the lifespan and their inherently linked physiology means one should not be considered independently of the other. The appropriate muscle outcome measure that can be incorporated into routine clinical care is still unknown. Low muscle mass, defined as a low skeletal muscle index on lumbar CT scan, has been reported in >50% of adult CD patients at diagnosis  and duriIn paediatric IBD, deficits in muscle mass Z-scores are common among newly diagnosed patients ,121,122 Physical activity and exercise regulate bone largely via the influence of mechanical loading. Bone tissue is sensitive to its biomechanical environment and adapts accordingly to mechanical loading or disuse, as described by Frost’s Mechanostat theory . Bone adLow levels of habitual physical activity and exercise are highlighted in observational data from self-reported ,129,130 The potential role of exercise in alleviating musculoskeletal burden in IBD has been little studied to date. In two exercise intervention studies, twelve months of low-impact core strengthening exercise  and six Several imaging studies have been employed attempting to elucidate the musculoskeletal phenotype in different IBD populations. The majority of published studies in IBD have employed dual-energy absorptiometry (DXA) for the assessment of bone density. Given that short stature and delayed growth and puberty are more common in paediatric chronic diseases like IBD, areal bone density will under-estimate bone mass and therefore the need for appropriate size adjustment is now recognised. As mentioned, this is performed by volumetric adjustment of spine bone density data or adjusting for height, lean mass, or bone age for bone density data at other sites ,139,140.The greater risk of adverse musculoskeletal health in CD compared to UC is reflected in the preponderance of CD patients included in published bone imaging studies. The complexity of contributing factors to bone health is highlighted by some conflicting data in adult studies, whereas the skeletal phenotype appears more consistent in studies of paediatric IBD. The currently understood skeletal phenotype in paediatric IBD, predominantly in paediatric CD, is characterised by deficits in cortical bone geometry with low volumetric bone density (meaning adjusted for size) but maintained or slightly elevated cortical volumetric bone density. Abnormalities are evident at diagnosis in paediatric IBD, with deficits in total areal bone density ,14,143, In adults with IBD, imaging studies of bone health have been almost exclusively conducted using DXA, excluding two high-resolution pQCT (HR-pQCT) ,145 and Imaging assessments of skeletal macro and microstructure is less common in adults compared to paediatric IBD. Two HR-pQCT studies reported deficits in trabecular microarchitecture, volumetric bone density, and cortical geometry, in young  and middThe persistence of musculoskeletal deficits in children and adults with IBD, particularly CD, despite advances in disease management and pharmacological therapies, highlights the need for research into effective therapeutic interventions which may include nutritional, lifestyle and pharmacological approaches. Research into understanding the patients who are at greatest risk of poor musculoskeletal outcomes using a genetic and epigenetic approach may allow the introduction of personalised therapies in the future.In adults with IBD treated with glucocorticoid, the use of anti-resorptive  bisphosphonate therapy is associated with attenuated bone loss and reduced fracture risk compared to non-treated controls . In the Particularly in younger IBD patients at risk of musculoskeletal morbidity, the role of non-pharmacological approaches like nutritional support and exercise therapy as an adjunct to current IBD treatment strategies should be explored in high-quality clinical trials. Future research on how these interventions can be incorporated into the routine clinical care of IBD patients is also important to facilitate maximum impact on health and disease outcomes.Multiple factors which include the underlying disease activity, nutritional issues, and use of glucocorticoids can impact musculoskeletal outcomes via diverse mechanisms in children and adults with IBD. Many patients despite good IBD symptom control have persistent deficits in muscle and bone health in published cohorts. Research into strategies to improve future musculoskeletal outcomes in IBD focusing on optimising disease targets beyond symptoms, plus improving nutritional status and beneficial lifestyle measures should now be prioritised.


Immunotherapy of malignant tumor is a verified and crucial anti-tumor strategy to help patients with cancer for prolonging prognostic survival. It is a novel anticancer tactics that activates the immune system to discern and damage cancer cells, thereby prevent them from proliferating. However, immunotherapy still faces many challenges in view of clinical efficacy and safety issues. Various nanomaterials, especially gold nanoparticles (AuNPs), have been developed not only for anticancer treatment but also for delivering antitumor drugs or combining other treatment strategies. Recently, some studies have focused on AuNPs for enhancing cancer immunotherapy. In this review, we summarized how AuNPs applicated as immune agents, drug carriers or combinations with other immunotherapies for anticancer treatment. AuNPs can not only act as immune regulators but also deliver immune drugs for cancer. Therefore, AuNPs are candidates for enhancing the efficiency and safety of cancer immunotherapy. Cancer immunotherapy has rapidly emerged in the past few years . The ultimate application of nanomedicine is to reprogram or to regulate immune responses by accurately targeting biological pathways . Hence, Gold nanoparticles (AuNPs) have attracted much attention due to their unique advantages among nanoparticles . In addiFirst, gold nanoparticles are a kind of biologically inert material suitable for medical applications with strong plasticity . Even deThe application of nanotechnology is mainly based on the early detection and diagnosis of tumors by nanodevices that can selectively target chemotherapeutic drugs and deliver them to specific tumor sites. The special properties of AuNPs have long been regarded as potential tools for the diagnosis of various cancers and drug delivery applications. These properties include a high surface area to volume ratio, surface plasmon resonance, surface chemistry and multifunctionalization, facile synthesis, and a stable nature. Various types of drugs can be immobilized on the surface of AuNPs, most notably by direct -S or -N binding, ligand bonding, and adsorption by electrostatic interaction, van der Waals forces, and hydrogen bonding. In general, because of the stronger interaction between Au and S, -N binding holds more promise for delivering drugs in cancer cells than -S binding . MoreoveTumor-associated antigen (TAA) reactivates the body's immune response to tumor cells and plays a vital role in the early prevention and treatment of cancer. In general, tumor vaccines may mainly consist of TAAs and adjuvants . Compare+ T-cell responses. HA-OVA-AuNPs promote antigen uptake by DCs through receptor-mediated endocytosis. HA-OVA-AuNPs show the ability to enhance NIR absorption and thermal energy translation. Cytosolic antigen delivery is allowed via the photothermally controlled process of partial heat-mediated endo/lysosome disruption by laser irradiation with reactive oxygen species generation, which helps to increase proteasome activity and downstream MHC I antigen presentation. Therefore, the HA-OVA-AuNP nanovaccine can effectively stimulate a potent anticancer immune response under laser irradiation. In another model of AuNPs mobilized with α-mannose as carriers for a TLR7 ligand to target immune cells  . They shne cells . The smain vivo. This approach can also induce the expansion of CD8+ T lymphocytes and NKG2D + NK cell subsets. The nanoparticles also had synergistic effects on the maturation and antigen presentation of DCs derived from bone marrow.Kang et al.  designedAuNPs, as carriers of tumor vaccines, play an important role in antitumor immunotherapy. Taken together, AuNP‐based vaccines are novel and efficient antitumor treatments .The delivery of small interfering RNA (siRNA) mediated by nanocarriers has provided a novel method of intracellular antigen synthesis, which has great application value in antitumor immunotherapy . Under nIn the research of + and CD4+ T cells in tumor tissues and spleen, thereby promoting immunotherapy. Its tumor suppression efficiency is much higher than that of PD-L1 antibody  and macrophages, and they mediate innate immune response, which can play the pro- and anti-tumor effect depending on the inflammatory mediators and cytokines in TME . The groin vitro cell model. They used primary macrophages and DCs of mice as an APC model. Even though AuNPs indistinctively changed the functions of macrophages and DCs, AuNPs had different effects on the response of macrophages and DCs to subsequent stimulation. Firstly, the secretion level of cytokines and chemokine were altered in DCs and macrophages. Moreover, antigen presentation to T cells increased when DCs came into contact with AuNPs resulting in stronger Th1, Th2 and Th17 responses. That is why we emphasize that immunotherapy plus AuNPs do not directly alter the immune response but indirectly influence the function of DCs and macrophages by modulating abnormal components in TME to get twice the result with half the effort. Therefore, AuNPs have been widely designed as drug delivery system.However, what worth noting is that AuNPs alone may be unlikely to directly impact the immune response, because their effects on the immune system remain ill-defined. Abnormal appearance and impaired function are the most common features of tumor vasculature . Hypoxiain vitro. These results prove that AuNPs inhibit angiogenesis by blocking VEGF-VEGFR2 signaling from TME cells to endothelial cells.If abnormal tumor vasculature is the key issue that hinders the implementation of tumor treatment, then vascular normalization strategies could be a promising solution. Another problem coming to us subsequently is that the therapeutic efficacy of antiangiogenic therapy has been obstructed by acquired resistance of the endothelium toward antiangiogenic drugs such as anti-VEGF therapy . Zhang eAlthough the antiangiogenic mechanism of AuNPs is still unclear, +CD8+ T lymphocytes that the immunotherapeutic response was enhanced by decreasing vascular permeability and improving vascular perfusion. Thereby, the vascular normalization strategy of AuNPs has great potential for tumor immunotherapy.Successful immunotherapy requires not only the infiltration of immune cells, but also an immune-supporting microenvironment to maintain the proliferation and function of  T cells. Normalization of blood vessels provides such an environment for immunotherapy . It is aAuNPs, with their multiple unique functional properties and ease of synthesis, have attracted extensive attention in antitumor tactics. Their inherent features can be altered by changing the characterization of the nanoparticles, such as shape, size and aspect ratio. They can be applied to a wide range of medical applications, especially photothermal therapy (PTT) and photodynamic therapy (PDT) . AuNPs eThe reason why PTT is available under the stimulation of pulsed or continuous-wave Doppler (CW) visible lasers is the surface plasmon resonance (SPR) absorption of AuNPs in the visible region, whereby such treatment may be suitable for shallow tumors  . Despite2) from tissues. If PDT desires to function successfully, it is completely dependent on the availability of O2 in tissues. In the process of PDT, the PS absorbed by the tissue is excited by laser light of a specific wavelength. Irradiating the tumor site can activate PS, which selectively accumulates in the tumor tissue, triggering a photochemical reaction to destroy the tumor. The excited PS will transfer energy to the surrounding O2 to generate reactive oxygen species (ROS) and to increase ROS levels in the target sites. ROS can react with adjacent biological macromolecules to produce significant cytotoxicity, cell damage, and even death or apoptosis  is elicited by damage-associated molecular pattern (DAMP) release, which induces DC maturation and effector cell activation, thereby strongly evoking systematic antitumor immune responses against mTNBC. Hence, this nanosystem offers a promising approach to ablate primary tumors while preventing tumor metastasis through immunogenic endoscopic effects.PDT is another light therapy developed in recent decades to destroy cancer cells and pathogenic bacteria . PDT invpoptosis . Liang eIn addition to PTT and PDT, radiation therapy (RT) is one of the least invasive and commonly used methods in the treatment of various cancers . RT invoThe use of nanoparticles in therapeutic applications has been improved by coating gold with organic compounds, such as amino acids and amino sugars, which act as carriers to transport nanoparticles to tumor cells . Colloidin vivo are not abundantly clear because few teams have tested them in clinical trials. Thus, there is controversy and disagreement about the potential of AuNPs for clinical use at present (However, very few clinical trials are being actively carried out for the approval of AuNPs for cancer diagnostics and therapy . As desc present .The advantages of AuNPs make them more effective in the immunotherapy of malignant tumors. For example, the ability to accumulate at tumor sites and the photothermal properties can enable the efficient and targeted delivery of genes, cancer vaccines, immune antibodies and other immune therapeutic-related components. Some studies have shown that in combination with current popular fields of tumor immunotherapy, such as PD-1/PD-1 AuNPs demonstrate excellent efficacy and show great potential clinical application value . It has in vivo during long-term treatment, causing uncertain side effects. Second, the application of the photothermal effect of AuNPs is often limited by the penetration depth of near-infrared lasers, and the heating intensity will decrease with increasing laser penetration depth, which limits the directional drug release effect and immunoregulatory activity of AuNPs. More importantly, a series of pharmacokinetic and histocompatibility parameters of AuNPs are changed by surface modification (However, AuNPs have various disadvantages in their application . First, fication .Therefore, AuNPs are candidates for enhancing the efficiency and safety of cancer immunotherapy. Every modification of the surface radical groups and ligands of AuNPs requires a reevaluation of their pharmacological and toxicological effects, which increases the amount and cycle of related drug research and development work. Nevertheless, the shortcomings of AuNPs have not deterred researchers from using them to improve the efficacy of tumor immunotherapy. In the future, there will be a series of works to modify the characteristics of AuNPs to overcome these shortcomings.


Amyloidosis is a protein deposition disease whereby a variety of precursor proteins form insoluble fibrils that deposit in tissues, causing organ dysfunction and, many times, death. Accurate characterization of the disease based on the nature of the precursor protein, organ involvement, and extent of disease is paramount to guide management. Cardiac amyloidosis is critical to understand because of its impact on prognosis and new treatment options available.New imaging methods have proven to be considerably valuable in the identification of cardiac amyloid infiltration. For treating clinicians, a diagnostic algorithm for patients with suspected amyloidosis with or without cardiomyopathy is shown to help classify disease and to direct appropriate genetic testing and management. For patients with light chain disease, recently introduced treatments adopted from multiple myeloma therapies have significantly extended progression-free and overall survival as well as organ response. In addition, new medical interventions are now available for those with transthyretin amyloidosis.Although cardiac amyloidosis contributes significantly to the morbidity and mortality associated with systemic disease, new tools are available to assist with diagnosis, prognosis, and management. Amyloidosis is a collection of diseases characterized by the progressive extracellular deposition of insoluble fibrillar proteins in tissues, resulting in organ dysfunction, and, many times, death . All amyThe diseases are highly varied; therefore, accurate description and characterization are critical. Cardiac involvement from amyloid deposition is relatively common and a major cause of morbidity and mortality. In this review, the major subtypes of amyloid will be examined, as well as a discussion on amyloid-related heart disease with updates on diagnostic tools and management options.Amyloidosis is first categorized as either systemic or localized. In localized disease, amyloid fibrils composed of light chains deposit in just one organ because of in situ production of amyloidgenic protein by a population of clonal B cells in the affected tissues. Common sites of localized disease include the bladder, respiratory tract, and skin. Localized amyloidosis can also involve the gastrointestinal tract and needs to be carefully discerned from systemic disease . LocalizAmyloidosis is further classified by the nature of the innate precursor protein that misfolds to form the fibrillar deposits. The major types of amyloidosis are light chain (AL), transthyretin-derived (ATTR) amyloidosis, and secondary amyloidosis (AA). There are now more than 30 proteins known to form amyloid in humans . SystemiFinally, the nature and degree of organ involvement further define a patient’s illness, with impact on initial presentation, management, and prognosis. Amyloid fibrils from a systemic process can deposit anywhere in the body except the central nervous system. Clinical features are non-specific, mimicking more common disease presentations, often leading to a delay in diagnosis. The kidneys are commonly involved in several forms of systemic disease. Albuminuria and nephrotic syndrome are the classic presentations, but renal dysfunction may also be present. Cardiac involvement occurs in about 50% of patients and is the leading cause of morbidity and mortality . AmyloidAcquired amyloidosis is more common than hereditary subtypes. AL is the most common, affecting 10–12 persons/million per year. Wild-type ATTR amyloidosis occurs predominately in men >70 years of age and primarily impacts the heart, although soft tissue involvement is often seen. Studies suggest up to 10–15% of older adults with heart failure may have unrecognized wtATTR . It mostThe most common form of hereditary amyloidosis results from a mutation of the TTR gene inherited in an autosomal dominant manner. More than 130 mutations of the TTR protein have been identified, the majority of which are noted to cause systemic amyloidosis, mainly impacting the peripheral nervous system and the heart . HereditCardiac amyloidosis is the leading cause of morbidity and mortality in patients with systemic disease but remains underdiagnosed. In a recent study of 108 patients with heart failure with preserved ejection fraction (HFpEF), endomyocardial biopsy demonstrated that 14% of these patients had cardiac amyloidosis . PatientPatients who may have cardiac amyloidosis should have a comprehensive evaluation with an EKG, echocardiogram, and laboratory evaluation including serum troponin as well as NT-ProBNP, biomarkers used for staging . BecauseWhereas cardiac biomarkers may assist in the diagnosis and management of cardiac amyloidosis, they are highly non-specific. NT-proBNP levels rise with a multitude of causes of diastolic heart failure or volume overload, including renal disease. Troponin is a marker of myocardial injury or stress from elevated myocardial oxygen demand, thus also non-specific; therefore, the use of cardiac imaging becomes paramount in the diagnosis of amyloid heart disease. Primary modalities utilized include echocardiography, cardiac magnetic resonance (CMR) imaging, and radionuclide imaging.Echocardiography in patients with cardiac amyloidosis often demonstrates left ventricular hypertrophy (LVH) with a sparkling appearance see Fig. . Left veElectrocardiography may demonstrate low voltages and pseudoinfarct patterns •. When cIn addition to echocardiography, cardiac magnetic resonance (CMR) imaging is immensely valuable in aiding the diagnosis of cardiac amyloidosis. Unlike echocardiography, which has fairly limited tissue characterization power, CMR allows for detailed examination of the myocardial interstitium and evaluation of characteristic changes on late gadolinium enhancement (LGE) imaging, rendering CMR far more sensitive and specific than echocardiography . Althoug99mTechnetium labeled bone-avid bisphosphonate derivatives has emerged as a highly sensitive and very specific technique to diagnose ATTR cardiac amyloidosis [99mTc-labeled pyrophosphate (PYP), 3,3-diphosphono-1,2-propano-dicarboxylic acid (DPD), and hydroxymethylenediphosphonate (HMDP), with 99mTc-PYP being the most commonly used tracer in the USA. It is unclear why there is greater uptake of 99mTc-PYP in hearts afflicted with ATTR, but it has been postulated that this increased uptake may be due to a higher calcium content or the composition of amyloid fibrils in ATTR compared to other forms [99mTc-PYP scan without monoclonal proteins in the blood and urine may be diagnosed with ATTR without tissue confirmation. The specificity and positive predictive value of this modality is >98% [99mTc-PYP/DPD/HMDP scanning [Endomyocardial biopsy has been the gold standard for pathologic confirmation of cardiac amyloidosis, but radionuclide scintigraphy with loidosis •. Moreovloidosis . The threr forms . Patient is >98% •. If a pscanning •. An elescanning •. A diag11C-Pittsburgh compound B (PIB) was developed for beta-amyloid imaging, but use is limited to sites with a cyclotron [18F-florbetapir and 18F-florbetaben are two other commonly used PET radiotracers proven to distinguish cardiac amyloidosis from other causes of cardiac hypertrophy [Positive emission tomography (PET) is emerging as another imaging modality that may prove useful in the identification of cardiac amyloid infiltration. With multiple tracers under development and investigation, PET utilizes radiotracers that directly bind to amyloid fibrils. yclotron •. 18F-flertrophy , 37.123I-meta-iodobenzylguanidine (mIBG), a modified analogue of norepinephrine stored in presynaptic nerve terminals within the cardiac conduction system, has been used to image myocardial denervation [123I-mIBG imaging is not helpful in diagnosing cardiac amyloidosis, it can be used to detect myocardial denervation earlier than detection of myocardial amyloid by 99mTc-PYP/DPD/HMDP scanning in patients with an hATTR mutation [Although development of various arrhythmias has been attributed to direct myocardial infiltration, this phenomenon may also be related to amyloid protein deposition impacting the cardiac innervation system. Autonomic dysfunction has been notably more common in patients with ATTR cardiac amyloidosis, particularly hATTR . 123I-meervation •. Althoumutation .The diagnosis of cardiac amyloidosis can be challenging and is often delayed. Cardiac imaging techniques as described are often neither sensitive nor specific, and the need for tissue biopsy with proper histological confirmation requires specialized centers and expertise. The diagnosis of wtATTR amyloidosis is particularly challenging because it presents at an older age often in patients with comorbidities. Furthermore, in contrast to hATTR and AL, there is an absence of supportive biomarkers in wild-type ATTR, including a TTR gene mutation or monoclonal gammopathy, respectively , 41. WitMaking a diagnosis of amyloidosis must first begin with a clinical suspicion see Fig. . Two feaPathologic confirmation of amyloid deposition is the gold standard for diagnosing amyloidosis. In those with systemic disease, targeting the affected organ can be associated with complications; therefore, it is preferable to begin with a lower-risk screening procedure. An abdominal fat pad aspiration can be performed easily in the outpatient setting. With an overall sensitivity of 80%, the subcutaneous fat pad aspiration is the preferred method for detecting systemic amyloidosis . It is iOnce amyloid protein is identified, it is critical to correctly identify the precursor amyloidgenic protein and thus the pathologic subtype of disease. Immunohistochemistry or immunofluorescence can be used in this effort, but these techniques are often unreliable . In AL, Treatment options for patients with systemic amyloidosis have improved tremendously in recent years. In general, all treatments for systemic amyloidosis focus on targeting the underlying production of amyloidgenic protein. In AL amyloidosis, treatments have been adopted from those used in multiple myeloma to target the underlying plasma cell dyscrasia. For upfront management, selection of a treatment should deliver the most rapid and effective reduction in monoclonal protein that the patient can safely tolerate while taking into consideration severity of organ involvement, characteristics of the clone, and the patient’s comorbidities . Early aOptions for treatment of AL amyloidosis include traditional chemotherapy as well as high-dose melphalan, followed by autologous stem cell transplantation (ASCT). Careful selection of those eligible for high-dose therapy is critical in reducing treatment-related mortality, and only about 20% of patients are candidates for ASCT on initial presentation. More may become eligible after effective upfront therapy; however, in some patients, upfront chemotherapy may induce toxicity and render a once transplant-eligible patient subsequently ineligible. For those with excellent performance status and ≤10% plasma cell burden at presentation, proceeding directly to ASCT without any induction is an option , but indFor transplant-ineligible patients, melphalan and dexamethasone had been standard ; howeverFor patients with relapsed AL amyloidosis, there is no clear regimen of choice, and careful selection of anti-plasma cell strategies used for multiple myeloma should be considered on the basis of the burden of disease, prior exposures, and the patient’s comorbidities. Immunomodulatory drugs including lenalidomide and pomalidomide are effective but often tolerated at lower doses than those used for multiple myeloma , 64. A pFor those with ATTR amyloidosis, treatment is also focused on reducing the supply of precursor protein for amyloid fibril formation. Until recently, there were no FDA-approved medications to either reduce the concentration of amyloid fibrils or prevent misfolding of TTR and thus decrease amyloid formation and prevent further organ damage. The mainstay of treatment was liver transplantation. In 2018, two new small molecule oligonucleotides that inhibit the synthesis of TTR were approved for the treatment of hATTR amyloidosis in patients with a confirmed genetic mutation and peripheral neuropathy. Patisiran, a small molecule RNA interference agent, was compared to placebo in patients with hATTR and polyneuropathy. Subjects receiving patisiran had reduced levels of TTR as well as significantly improvement of peripheral neuropathy scores and quality of life . A similAnti-amyloid strategies to treat fibril deposition in damaged organs have unfortunately failed to yield favorable results. Efforts are continuing, however, and trials exploring doxycycline (NCT02207556) and an anti-fibril monoclonal antibody, CAEL-101 , are ongoing.Although amyloidosis remains rare, both acquired and inherited subtypes are likely underdiagnosed. As new treatments become available and outcomes improve with the advent of novel approaches to target the production of amyloidgenic proteins, it is increasingly important for physicians to identify patients with amyloidosis and characterize the subtype accurately. Cardiac amyloidosis contributes significantly to the morbidity and mortality associated with systemic disease, and new tools are available to assist with diagnosis, prognosis, and management.


Despite improvements in the treatment of several cancer types, the extremely poor prognosis of pancreatic cancer patients has remained unchanged over the last decades. Therefore, new therapeutic regimens for pancreatic cancer are highly needed. In this review, we will discuss the potential of induced pluripotent stem cells (iPSCs) to generate representative pancreatic cancer models that can aid the development of novel diagnostics and therapeutic strategies. Furthermore, the potential of iPSCs as pancreatic cancer vaccines or as a basis for cellular therapies will be discussed. With promising preclinical results and ongoing clinical trials, the potential of iPSCs to further the treatment of pancreatic cancer is being explored and, in turn, will hopefully provide additional therapies to increase the poor survival rates of this patient population.Advances in the treatment of pancreatic ductal adenocarcinoma (PDAC) using neoadjuvant chemoradiotherapy, chemotherapy, and immunotherapy have had minimal impact on the overall survival of patients. A general lack of immunogenic features and a complex tumor microenvironment (TME) are likely culprits for therapy refractoriness in PDAC. Induced pluripotent stem cells (iPSCs) should be explored as a means to advance the treatment options for PDAC, by providing representative in vitro models of pancreatic cancer development. In addition, iPSCs could be used for tailor-made cellular immunotherapies or as a source of tumor-associated antigens in the context of vaccination.  Establishment of complex multicellular models that represent the PDAC TME could open up avenues to test therapeutic interventions.Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, accounting for more than 90% of all cases . PDAC haIn recent years, we have seen cancer immunotherapies, in particular immune checkpoint inhibitors, redefine the potential of cancer treatments as they have produced remarkable improvements in the clinical outcome of patients with various cancer types ,19,20,21Here, we discuss induced pluripotent stem cell (iPSC)-based models as a promising tool to model PDAC development and treatment responses. Aside from their use as models, they also hold great potential for novel immunotherapies.iPSCs provide excellent possibilities for the modeling of PDAC and serve as a unique source of tumor-associated antigens for whole-cell cancer vaccines and immune cells for adoptive cellular immunotherapies. Pluripotent stem cells hold stemness, which is defined as the ability to proliferate indefinitely while maintaining pluripotency. Embryonic stem cells (ESCs) are a classic example of this capability and are capable of differentiating into all of the three embryonic germ layers, thus giving rise to all adult cell types ,35. SincAround 20% of PDAC patients in the United States are diagnosed with localized disease and are therefore eligible for surgical resection . HoweverG12DKRAS mutation and a deletion of CDKN2A. The oncogenic KRAS mutations are the most frequently detected oncogenic alteration in PDAC, being observed in >90% of patients ..+ and CDG12D and p53R172H were introduced in murine iPSCs derived from healthy cells followed by controlled differentiation into PDAC progenitor cells. These cells were antigenically comparable to PDAC cells from the KPC mice and, therefore, serve as a good repertoire of PDAC-expressing antigens. The iPSC-induced PDAC progenitor cells were infected with a non-replicating oncolytic virus to enhance the immunogenicity of the final vaccine formulation. Importantly, the immunogenicity of the vaccine demonstrated a clinical impact by delaying tumor development and prolonging survival of the KPC mice vaccinated before tumor development. However, the vaccine failed to provide complete protection from tumor development in the mice. Upon vaccination, CD8+ T cell infiltration increased in the PDAC TME and an accumulation of central memory T cells was observed in the secondary lymph nodes. Furthermore, splenocytes from vaccinated mice, prior to tumor development, showed enhanced production of the proinflammatory cytokine IFNγ after ex vivo challenge with tumor-cell lines derived from the corresponding model. This iPSC-based autologous prophylactic cancer vaccination regimen demonstrates promising results in a mouse model of PDAC and further studies will clarify whether iPSC-based prophylactic PDAC vaccines have a potential for the prevention of pancreatic malignancies in at-risk individuals.Prophylactic vaccines can play a role in the prevention of cancers caused by viruses, e.g., human papillomaviruses (HPVs) and the hepatitis B virus (HBV). To date, no prophylactic vaccines aiming at preventing non-viral-related cancers have been approved. Lu et al. (2020) reported an iPSC-based autologous prophylactic cancer vaccination regimen that was evaluated using the KPC mouse model of PDAC with spontaneous tumor development . The PDAThe first clinical use of iPSC-based cancer vaccines is yet to be seen, but the above-mentioned preclinical studies demonstrate the potential of these vaccines to elicit anti-PDAC immune responses. In addition to PDAC therapy, iPSC-based antitumor vaccination could potentially serve as a promising universal approach in a broad spectrum of cancer types, including mesothelioma, breast cancer, and melanoma (reviewed in ) 65,97,,97,98,99Current approaches focusing on T cell immunotherapies, such as autologous T cell transfer, engineered T cell receptor (TCR) T cells, and CAR T cells, typically require autologous cell manufacturing for each individual patient. Therefore, there is an unmet need for innovative cell sources to broaden the application of cellular immunotherapies . iPSCs cCAR cells serve as an innovative approach to support the recognition and killing of cancer cells in a human leukocyte antigen (HLA)-independent manner, thereby circumventing tumor escape mechanisms associated with defects in antigen presentation (reviewed in ). CD19-dTumor escape from CAR cells due to the complex immunosuppressive TMEs, as observed in PDAC, remains an obstacle for CAR cell therapies, and strategies to overcome this have been proposed . One strDespite being in the early stages, iPSC-derived NK cells  and NKT cells  are currently being clinically investigated in patients with advanced solid tumors. It is worth noting that PDAC patients will be included for monotherapy with the iPSC-derived NK cells or as a combinatorial therapy with immune checkpoint inhibitors. With the increasing availability of promising preclinical data, it is plausible that additional clinical trials will be initiated in the near future. However, before clinical implementation, all safety issues must be thoroughly addressed , whPDAC has an extremely poor prognosis, which has remained unchanged over the last decades. Therefore, novel strategies for the modeling and treatment of PDAC are highly needed. iPSCs provide innovative options for the modeling of precancerous lesions as well as the early development and progression of PDAC, which can improve knowledge of clinical-relevant biomarkers early after the onset of disease. Furthermore, iPSCs constitute a novel consistent source of a variety of cells for studying this disease in complex systems. In recent years, increased attention has been giving to the TME due to its important role in the success of immunotherapies (reviewed in ). iPSC-b+ CD8+ T cells in the tumor-draining lymph nodes as well as in the TME [Most PDAC patients are refractory to immune checkpoint inhibitors, which is likely due to a low mutational burden and strongly immunosuppressive TME, even though neoantigen-specific effector T cells have been demonstrated in the TME of PDAC patients ,30. A ra the TME ,136. Lik the TME . If a siThe pluripotency and indefinite proliferation capacity of iPSCs allow these cells to serve as a consistent cell source for various types of adoptive cellular immunotherapies. Additionally, hypoimmunogenic derivates of iPSCs have been reported, thus providing great potential for the generation of an iPSC line as a universal donor for off-the-shelf cellular immunotherapies . ConsequA note of caution is warranted regarding the reprogramming and differentiation of iPSCs. First, iPSCs possess the risk of transcriptional and genetic alterations. Therefore, controlled induction of iPSCs is essential to ensure a homogenous population for further applications. Second, to reduce potential risks of tumorigenesis, the final cell product of iPSC-derivates should include a pure population of differentiated cells without remnant undifferentiated iPSCs that could form teratomas.With the promising preclinical results for iPSC-based immunotherapies and two clinical trials ongoing, including one with PDAC patients, it seems plausible to expect more clinical trials to be initiated in the near future. Hopefully, these trials will advance the treatment options for PDAC patients and, in turn, increase their survival rates.