Resistance to therapeutic agents, either intrinsic or acquired, is currently a major problem in the treatment of cancers and occurs in virtually every type of anti-cancer therapy. Therefore, understanding how resistance can be prevented, targeted and predicted becomes increasingly important to improve cancer therapy. In the last decade, it has become apparent that alterations in cellular metabolism are a hallmark of cancer cells and that a rewired metabolism is essential for rapid tumor growth and proliferation. Recently, metabolic alterations have been shown to play a role in the sensitivity of cancer cells to widely-used first-line chemotherapeutics. This suggests that metabolic pathways are important mediators of resistance toward anticancer agents. In this review, we highlight the metabolic alterations associated with resistance toward different anticancer agents and discuss how metabolism may be exploited to overcome drug resistance to classical chemotherapy. Changes in metabolism are one of the emerging hallmarks of cancer cells . AlthougBoth intrinsic and extrinsic mechanisms contribute to the characteristic metabolic alterations in cancer cells. Many different oncogenic as well as tumor suppressor signaling pathways influence metabolism, such as hypoxia-inducible factor 1 (HIF1), p53 and MYC. In addition, cancer metabolism is influenced by the tumor microenvironment, for example the interaction with surrounding cells and the variation in availability of nutrients and oxygen, as extensively reviewed elsewhere , 5–12. TResistance to therapeutic agents, either intrinsic or acquired, is currently a major problem in the treatment of cancers and occurs in virtually every type of anti-cancer therapy , 16. AltRecent studies show that the response to widely-used first-line chemotherapy is substantially influenced by the metabolic state of the cells and that cancer cells rewire their metabolism in response to chemotherapeutic drugs. We postulate that metabolic rewiring is a novel and important mechanism of adaptive resistance. Here, we will introduce the main features of cancer metabolism in relation to drug resistance and review specific metabolic programs and adaptations that exist in drug-resistant tumors. We will discuss how these adaptations depend both on the drug and the origin of the tumor and how they contribute to drug resistance, focussing on widely-used chemotherapeutics, including proteasome inhibitors (multiple myeloma), EGFR inhibitors (breast cancer), cisplatin (lung cancer/ovarian cancer) and BRAF inhibitors (melanoma). Finally, we will illustrate how targeting metabolism could overcome drug resistance to standard chemotherapy.2, which can be oxidized in the electron transport chain (ETC) complexes to ultimately produce ATP in a process called oxidative phosphorylation (OXPHOS)  , 29  . Althoug(OXPHOS) . This phCancer cells sustain their high glycolytic rates in several ways. For example, glycolytic cancers often meet the high demand for extracellular glucose by overexpression of glucose transporters (GLUTs) , 32. TheWhy cancer cells prefer the less efficient glycolysis over OXPHOS for ATP production is not fully understood. Initially, Warburg hypothesized that cancer cells increase glycolytic activity because of impaired mitochondrial function . Indeed,One of the pathways that branches of glycolysis is the pentose phosphate pathway (PPP), which both sustains the biosynthesis of macromolecules and maintains redox homeostasis Figure  51–53).–53.51–53As intermediates of the TCA cycle are also building blocks for the biosynthesis of lipids and nucleotides Figure , the TCAO-GlcNAcylation . Glutamate in turn can be further converted to α-ketoglutarate that can subsequently enter the TCA cycle Figure . Via gluHowever, a range of other metabolites has also been described to fuel the TCA cycle in cancer. Fatty acids are not only important components of membranes, but are also energy-rich compounds that can be degraded to provide ATP via β-oxidation . CarnitiInterestingly, TCA cycle enzymes are emerging as mediators of malignant transformation in cancer. Fumarate hydratase (FH) and succinate dehydrogenase (SDH) are tumor suppressors Figure . Loss-ofIt is becoming increasingly clear that changes in metabolism influence drug response to established first-line chemotherapy in several cancers, identifying metabolic rewiring as a novel, and important mechanism of adaptive resistance. Table In this section, we provide a more in-depth analysis of the four cancer-drug combinations on which most research has been done: Proteasome inhibitors for multiple myeloma, EGFR inhibitors for breast cancer, cisplatin for lung and ovarian cancer, and BRAF inhibitors for melanoma.Proteasome inhibitors are a cornerstone in the treatment of multiple myeloma , 144, wiSeveral recent studies describe that metabolic processes are also involved in mediating sensitivity toward bortezomib  which is important for mitochondrial ROS clearing . The comin vitro and in myeloma-bearing mice  and DNA, resulting in increased ROS and DNA damage, which eventually leads to apoptosis . Many meCisplatin-resistant cells have an altered energy metabolism compared to sensitive cells, but the findings on glycolysis and oxidative phosphorylation in lung and ovarian cancers are opposing. Cisplatin-resistant ovarian and cervical cancer cells were found to have higher rates of glycolysis and reduced mitochondrial activity compared to their cisplatin-sensitive counterparts. This leads to a higher sensitivity of resistant ovarian cancer cells to glucose starvation or to treatment with 2 deoxyglucose (2-DG), a competitive inhibitor of HK , 108. CiInterestingly, the metabolic reprogramming in lung cancer cells seems to some extent specific for cisplatin. Lung cancer cells that are resistant to carboplatin, which has a similar mode of action, are more dependent on glycolysis . In addiMany breast cancers overexpress the receptor tyrosine kinase ErbB2 and several drugs that target ErbB2, such as trastuzumab and lapatinib, are used in the treatment of breast cancer. Several mechanisms of resistance against these targeted therapies have been described, including the re-activation of downstream kinase pathways and oncogenic signaling , 164. InIncreased glycolysis is a common feature of drug-resistant breast cancer cells irrespective of the type of chemotherapeutical agent used Table , but thiInterestingly, Park et al. showed that the nuclear receptor estrogen-related receptor alpha (ERRα) regulates a metabolic switch to allow breast cancer cells to use lactate as a substrate for mitochondrial respiration in the absence of glucose. The ability to bypass glycolysis makes these cells less vulnerable for PI3K/mTOR inhibitors in the presence of lactate and ERRα antagonists are able to restore drug efficacy , underscAnother metabolic aspect that is observed in drug-resistant breast cancer cells is increased levels of OXPHOS, coupled to higher levels of oxidative stress. For example, tamoxifen-resistant MCF-7 breast cancer cells display increased mitochondrial metabolism and ATP production . The bigMost cutaneous melanomas harbor activating mutations in the protein kinase BRAF, which makes inhibitors that target mutant BRAF promising agents to treat melanoma patients. In terms of metabolism, melanomas that express mutant BRAF and that developed resistance against BRAF inhibitors display increased activity of mitochondrial oxidative metabolism, increased dependency on mitochondria for survival and higher levels of ROS Table  120–123123, 126.From the studies discussed above, it becomes apparent that anticancer drug resistance to first-line chemotherapy is often linked to metabolic alterations and consequently, that these may be targeted to overcome drug resistance or to enhance the efficacy of current chemotherapy. Among the different drug-resistant cancers described, pathways involved in redox and energy metabolism are frequently altered Table , making Resistance to several anticancer agents, including proteasome inhibitors, cisplatin, EGFR inhibitors and BRAF inhibitors, is accompanied with increased activity of pathways involved in redox balance, suggesting that interfering with redox metabolism can improve response to a wide range of drugs and aid in overcoming multidrug resistance. A majority of anticancer agents induce apoptosis by increasing oxidative stress , 167–169In addition, many studies show an association between drug resistant cells and the Warburg effect, suggesting that a high glycolytic rate helps cancer cells to survive anticancer treatment, such as bortezomib, cisplatin and lapatinib , 170. AsFinally, most studies on metabolism-mediated drug resistance have so far focused on glycolysis and the TCA cycle and on the role glucose and glutamine. But fatty acids and branched chain amino acids can also provide energy and are also linked to tumorigenesis , 176. InIt is clear that understanding cancer metabolism can improve cancer therapy, as exemplified by the widespread use of 18-fluorodeoxyglucose, a glucose analog that exploits the Warburg effect in PET imaging for cancer diagnosis, treatment, and prognosis . The potIn this review, we discussed specific metabolic programs and adaptations that exist in drug-resistant tumors, how these adaptations depend both on the drug and the origin of the tumor and how they contribute to drug resistance. From these studies, it becomes apparent that for many first-line chemotherapeutic agents, combinational treatments with metabolic drugs hold great promise to increase drug efficacy. Moreover, a better understanding of the altered metabolism in different drug resistant cancers is essential to further improve cancer therapy. Such understanding will provide insights into the molecular mechanisms of resistance to identify novel metabolic targets that can be used for  therapy. Finally, this knowledge may also lead to prognostic biomarkers for drug response, which could advance current therapy by predicting drug response based on the metabolic state of a tumor and thereby contribute toward more efficacious personalized medicine.CB and EZ wrote the manuscript. Both authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Regulatory T cells (Tregs) are believed to be dysfunctional in autoimmunity. Juvenile idiopathic arthritis (JIA) and juvenile dermatomyositis (JDM) result from a loss of normal immune regulation in specific tissues such as joints or muscle and skin, respectively. Here, we discuss recent findings in regard to Treg biology in oligo-/polyarticular JIA and JDM, as well as what we can learn about Treg-related disease mechanism, treatment and biomarkers in JIA/JDM from studies of other diseases. We explore the potential use of Treg immunoregulatory markers and gene signatures as biomarkers for disease course and/or treatment success. Further, we discuss how Tregs are affected by several treatment strategies already employed in the therapy of JIA and JDM and by alternative immunotherapies such as anti-cytokine or co-receptor targeting. Finally, we review recent successes in using Tregs as a treatment target with low-dose IL-2 or cellular immunotherapy. Thus, this mini review will highlight our current understanding and identify open questions in regard to Treg biology, and how recent findings may advance biomarkers and new therapies for JIA and JDM. While JIA is the most common inflammatory rheumatic disease in children, JDM is rare. JIA is characterized by persistent arthritis and subtype-dependent symptoms [reviewed in ]. Here, Here, we discuss recent advances in the understanding of Treg biology in oligo-/ polyarticular JIA and JDM, and what we can learn about Treg-related disease mechanisms, treatments and biomarkers from other diseases.+FOXP3+ Tregs in JIA has been considerably characterized in the past (+ non-Treg cells (Tconv) are more clonal  the future disease course, (ii) treatment response, and (iii) the safety for medication withdrawal during clinical remission Figure .Inflammation markers in the serum can indicate disease activity and potentially treatment response in JIA [reviewed in ]. In JDM+) in PB was proposed as a biomarker for disease activity in arthritis  were found. Low expression of the immunoregulatory receptor CD39 has been suggested as an indicator of methotrexate resistance in RA Th17 cells with a Th1-like phenotype, function –74. UsteIL-6 also drives inflammatory environments, including skewing the Treg/Th17 balance toward Th17 . Anti-ILWhether drugs such as ustekinumab and tocilizumab act on Tregs directly or through changing the microenvironment is unclear. Human Tregs can express the receptors for IL-6 , IL-12 , and IL-Alternatively, co-receptors can be targeted to manipulate the immunoregulatory balance. Initially established for cancer therapy (checkpoint blockade), mimicking checkpoints such as CTLA4  is used as treatment for autoimmunity. Abatacept has been shown to be safe and effective in oligo- and polyarticular JIA , 89–92, Adoptive transfer of Tregs has been shown to be safe and possibly effective at reducing inflammation, inducing transplant tolerance, preventing graft-vs.-host disease (GVHD) and treating autoimmunity [reviewed in ].in vitro to home to specific sites  and SF aspirated during therapeutic joint injections (JIA). Novel techniques, such as single cell sequencing, multidimensional mass/flow cytometry and microscopy, will aid using clinical samples to their full potential –127. AddWhile there is progress toward unified measures of disease activity , 47, theSuccess of various agents affecting the immunoregulatory balance in other diseases point to potential uses in JIA and JDM. Any (new) therapy will need to be considered in regards to both effector cells AND Tregs, since some therapies might have unexpected effects on Tregs. Thus, it is important to continuously build our understanding of how various agents affect the immunoregulatory balance.In conclusion, important recent advances might lead to valid future contributions to the widened arsenal of treatment options available to restore the immunoregulatory balance in a heterogeneous disease spectrum.AP conceived the review. RH and AP reviewed the literature and co-wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The first description of Hippo signaling in mammals a little more than 10 years ago showed a striking phenotype in the liver, linking the role of this signaling pathway to organ size control and carcinogenesis. Even though Hippo signaling has been extensively studied in the liver and other organs over the recent years, many open questions remain in our understanding of its role in hepatic physiology and disease. The functions of Hippo signaling extend well beyond cancer and organ size determination: components of upstream Hippo signaling and the downstream effectors YAP and TAZ are involved in a multitude of cell and non-cell autonomous functions including cell proliferation, survival, development, differentiation, metabolism, and cross-talk with the immune system. Moreover, regulation and biological functions of Hippo signaling are often organ or even cell type specific – making its role even more complex. Here, we give a concise overview of the role of Hippo signaling in the liver with a focus on cell-type specific functions. We outline open questions and future research directions that will help to improve our understanding of this important pathway in liver disease. Drosophila and mammals, the importance of Hippo signaling in the liver became evident with a striking phenotype: overexpression of YAP or expression of activated YAP resulted in dramatic overgrowth of the liver, identifying Hippo signaling as an important determinant in organ size control , that have been shown to influence regeneration and fibrosis through angiocrine signaling to stellate cells in acute and chronic liver damage . Co-cultWith the emerging importance of Hippo signaling in HSC, LSECs, and possibly endothelial cells in fibrosis and injury, any considerations to therapeutically target this pathway should take into account that liver regeneration will likely be impaired – possibly exacerbating liver injury in the long run.In any tissue, cell proliferation and growth are dependent on nutrient availability. In this context, regulation of pro-proliferative YAP and TAZ by cellular metabolism is likely. Indeed, low energy levels trigger inhibition of YAP and TAZ, while high levels of glucose and fatty acids activate downstream YAP/TAZ transcription. On the other hand, YAP/TAZ themselves have been shown to promote key metabolic processes such as glycolysis to ensure an adequate energy supply in proliferating cells . In the Timp1, and Col1a1 (Sav1 in NAFLD-associated tumors, but not those that developed in viral hepatitis (In murine models of NAFLD and in samples of human steatohepatitis an increase of YAP/TAZ levels can be observed – mainly in regenerative ductular reactions. In mice, viral expression of activated TAZ promotes inflammation in the liver, NAFLD, and tumor formation, linking TAZ – but not YAP – to an inflammatory signature in tumor development . Mechanid Col1a1 . Interesepatitis . One mecepatitis . To dateCirrhosis and chronic inflammation in the liver are leading risk factors for liver cancer . While YGiven the increase in liver stiffness that occurs in cirrhosis, mechanical inputs are key candidates for oncogenic YAP activation that contributes to fibrosis progression and liver cancer. Mechanistically, several membrane-associated proteins such as Nf2/Merlin, scaffolding proteins of the angiomotin family, and WWC proteins have been shown to positively and – for certain members of the angiomotin family – negatively regulate Hippo signaling  – howeveMcp1 triggers accumulation of tumor-infiltrating macrophages that impair immune clearance of transformed hepatocytes and promote HCC development (Downstream of activated YAP/TAZ, unchecked proliferation and deregulated cell cycle control are one of the key mechanisms driving tumor development. Several studies showed that YAP/TEAD co-operate with E2F transcription factors downstream of retinoblastoma signaling to promote proliferation in cancer, including in liver cancer . While lelopment . If the elopment .in vivo – either to promote liver regeneration or to inhibit fibrosis and cancer progression – presents an unmet need in liver disease. Hepatic inactivation of MST kinases or YAP by microencapsulated siRNA as well as YAP silencing by AAV-delivered shRNA has yielded promising efficiency in murine models, but a transition into clinics is not foreseeable yet (Effective targeting of Hippo signaling able yet . Recentlable yet .Nf2-deficient livers (in vivo YAP inhibition (in vitro, their efficiency in the liver cells is not known and the toxicity associated with interference of the cytoskeleton will likely limit their in vivo use (In contrast to regeneration, inhibition of YAP/TAZ is a promising therapeutic approach in cirrhosis, steatohepatitis, or hepatocellular carcinoma. Outcomes of YAP activation in the liver are mostly dependent on its interaction with TEAD transcription factors, making YAP/TEAD complexes a promising target. Verteporfin, a substance identified in a compound screen for YAP/TEAD inhibitors, impeded HCC development in t livers . Howevert livers . Recentlhibition . In esophibition . Howeverhibition . In HCC hibition . While cvivo use . In Wnt-vivo use , while ivivo use . Again, Our knowledge about Hippo signaling in different hepatic cell types has expanded over the recent years (summarized in SM and UE drafted and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


PCR involves a repeating cycle of replication to amplify small segments of deoxyribonucleic acid (DNA). A novel application of this technique is microbial identification in infectious keratitis, one of the leading causes of blindness in the world. PCR is more sensitive than biological stains and culture, which are considered the current gold standards for diagnosing infectious keratitis. The diagnosis and treatment of infectious keratitis cost the United States millions of dollars in health expenditure. PCR may help offset that cost by allowing for individualized disease management and screening for multiple antibiotic-resistant genes. While beneficial, PCR demonstrates lower specificity rates compared to culture and stain, indicating its shortcomings; this can be overcome by performing PCR after narrowing the pool of potential microorganisms. This article examines the clinical utility of PCR in cases of infectious keratitis by evaluating its reliability, validity, associated costs, and indications. Infectious keratitis is characterized by inflammation of the cornea due to infection by bacteria, fungi, amebae, and viruses . It is iThe purpose of this article is to examine the practical applications, diagnostic validity, and cost-effectiveness of PCR in infectious keratitis.To find information on PCR and infectious keratitis, a literature search was performed using the following sources: PubMed, Google Scholar, and Scopus with the keywords “PCR and infectious keratitis,” “polymerase chain reaction keratitis,” “PCR and bacterial keratitis,” “PCR and fungal keratitis,” “PCR and viral keratitis,” and “PCR and keratitis.” Reference lists of these articles were used to find additional articles. There were no language restrictions. Publications were drawn between the dates of 1990-2019.PCR AdvantagesPCR involves repeated cycles of denaturation, amplification, and replication, in which segments of deoxyribonucleic acid (DNA) are continuously multiplied. Specific DNA primers are employed to indicate the presence of the microorganism in question . PCR hasMultiple reports have shown PCR to be comparable, if not better than, traditional staining and culturing 4, 15-17]. Gram staining can correctly identify bacteria and fungi 60-75% and 35-90% of the time, respectively , 18. Cul. Gram stPCR DisadvantagesPCR has a few shortcomings . Its speCost-EffectivenessCulturing and staining have been used for decades in large part due to their ease and accessibility. Staining and culturing are both inexpensive, with many of the materials widely available. In contrast, PCR requires trained technicians and specific machinery. Clinicians who wish to analyze PCR off-site can send samples to an outside laboratory, which can cost from 15 USD to 30 USD per microorganism tested . The genA cost-effective analysis comparing multiplex PCR to blood culturing of Candida infections showed a lower overall cost of PCR along with better health outcomes . The estPCR Use in Antibiotic ResistanceEmpiric therapy for infectious keratitis using broad-spectrum antimicrobials has led to antimicrobial resistance . For exaA benefit of PCR is the ability to test for multi-drug resistance. As shown with MRSA, a strength of multiplex PCR is the ability to test for multiple antibiotic genes with one sample, guiding specific antimicrobial treatment while reducing the chance of developing drug resistance . InteresThe future of PCR appears promising. New versions of the classic PCR have drastically shortened the time required to reach a diagnosis while also reducing the number of false positives [27-29]. Even in its present state, PCR has shown to match and even exceed the gold standard (culture and staining) in certain performance measures, such as sensitivity for the detection of microorganisms in infectious keratitis. The widely used broad-range PCR assay may distinguish among the various microbes. However, clinicians who wish to confirm clinical impression and consider cost-effectiveness may narrow the differential of causative organisms when sending samples to laboratories for PCR analysis. Ethical issues have been completely observed by the authors. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship of this manuscript, take responsibility for the integrity of the work as a whole, and have given final approval for the version to be published. No conflict of interest has been presented.Research to Prevent Blindness, NY, USA


High-throughput, massively parallel sequence analysis has revolutionized the way that researchers design and execute scientific investigations. Vast amounts of sequence data can be generated in short periods of time. Regarding ophthalmology and vision research, extensive interrogation of patient samples for underlying causative DNA mutations has resulted in the discovery of many new genes relevant to eye disease. However, such analysis remains functionally limited. RNA-sequencing accurately snapshots thousands of genes, capturing many subtypes of RNA molecules, and has become the gold standard for transcriptome gene expression quantification. RNA-sequencing has the potential to advance our understanding of eye development and disease; it can reveal new candidates to improve our molecular diagnosis rates and highlight therapeutic targets for intervention. But with a wide range of applications, the design of such experiments can be problematic, no single optimal pipeline exists, and therefore, several considerations must be undertaken for optimal study design. We review the key steps involved in RNA-sequencing experimental design and the downstream bioinformatic pipelines used for differential gene expression. We provide guidance on the application of RNA-sequencing to ophthalmology and sources of open-access eye-related data sets. For example, investigation of gene expression changes in corneal epithelial tissue from keratoconus patients has provided insights into the cause of this progressive corneal degeneration.2 Pathways including Wnt, Hedgehog, and Notch1 signaling were shown to be significantly reduced in keratoconus epithelium. In glaucoma, the leading cause of irreversible blindness worldwide characterized by the progressive loss of retinal ganglion cells (RGCs),4 investigations into the RGC transcriptome of induced pluripotent stem cells (iPSCs) from patients with the SIX6 risk allele  associated with reduced retinal nerve fiber layer thickness, and mouse models of optic nerve head damage have identified critical pathophysiologic pathways, such as endoplasmic reticulum stress, Notch signaling, and mammalian target of rapamycin (mTOR) pathway.57 Elucidation of transcript signatures in lens development has revealed the expression of novel transcripts decreasing in postnatal tissue.8 Lens-enriched expression analysis has confirmed high expression of established cataract-linked genes, such as the Crystallin gene family, and identified a number of transcription factors as novel potential regulators in the lens.9 RNA-seq of rod photoreceptors from the zebrafish has identified novel expression of genes not previously thought to be expressed in this cell type including opsin 4.1 and several nuclear hormone receptor genes.10 Similar experiments on dissociated mouse cones have provided an insight into the gene expression patterns occurring throughout postnatal development, highlighting 14% of all genes detected were switched off around postnatal day 6 (P6), including those encoding transcription factors, neurogenesis, and cone-specific genes.11 Such investigations reveal the role of previously unknown or unclassified transcripts in eye development, for example, the characterization of zebrafish zic2, which restricts pax2a expression and Hedgehog signaling, when ablated causes chorioretinal coloboma12 and identification of numerous miRNAs regulating pathways not previously associated with retinal degeneration, using retinal pigment epithelium (RPE) cells under oxidative stress as a model system.13 In this manner, novel information is gleaned; new targets for potential molecular diagnosis or therapeutic interventions may emerge.15 In this review, we will cover the considerations for the design and execution of a typical RNA-seq project investigating differentially expressed messenger RNA (mRNA). We will provide recent examples of the utilization of RNA-seq within the field of ophthalmology.With the advent of high-throughput sequencing technologies, focus on temporal gene expression through examination of the active transcriptome of tissues, cells, and model systems using RNA-sequencing (RNA-seq) has increased.With no single optimal pipeline for this experimentation, combined with no standard application and analysis approach, the use of RNA-seq data can be daunting. Experimental plan and strategic approaches depend highly on the type of RNA and or organism being studied, as well as the goals of the research. One may utilize previously reported species transcriptomes to guide the alignment of reads or align without prior knowledge to identify potentially novel transcripts.17 A growing number of algorithms can calculate the required sample number for significance and power of experiments; including Scotty,18 powsimR,19 PROPER,20 and RNASeqPower.21 Technical replicates are generally not required for differential expression analysis, as RNA-seq has been shown to be accurate as well as reproducible.2224One of the most crucial requirements for a successful RNA-seq experiment is the biological question of interest and how the data generated can answer that. 26 It is also possible to utilize transcriptomic data submitted to public repositories, such as EMBL ENA27 and National Center for Biotechnology Information (NCBI) SRA,28 to obtain information on the variance of data. 54 A significant source of false discovery of differential expression is commonly across batches of experiments rather than across the biological groups of interest.55For pilot studies to assess accuracy and variance of analysis at different stages of an RNA-seq pipeline, simulated data can be created through synthetic reads generated from genomic sequence.56 rRNA depletion is an essential consideration for formalin-fixed and paraffin-embedded (FFPE) samples where RNAs are potentially degraded to a small average size, under 200 nucleotides.57 rRNA depletion should also be considered when the biological sample cannot provide enough quantity or high-quality mRNA through poly(A) selection.59 For samples with a small amount of starting material, there are specific library preparation systems available, such as SMART-seq , relying on pre-amplification of fragments and may include a second stage of amplification.60 This can result in variable 3′ end bias representation of genes in library preparation, although the overall effect on expression values may be negligible.61 Small RNA species, such as those lacking poly(A) signals, can be assessed through small RNA-seq protocols.62Within our cells, several RNA species are present at any one time serving differing roles. Through transcription of genes, there are protein-encoding mRNAs. Small RNAs involved in translation include transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs). Regulatory RNA species, include antisense RNAs (asRNAs), microRNAs (miRNAs), Piwi-interacting RNAs (piRNAs), small interfering RNAs (siRNAs), short hairpin RNA (shRNA), and long noncoding RNA (lncRNA), all play a role in gene expression regulation. Highly abundant rRNA species, the predominant component of the ribosome involved in protein synthesis, constitutes up to 90% of the total RNA in cells. rRNA may require removal from samples to produce a library with considerably more representation of mRNA transcripts. Methods for rRNA removal include enriching mRNA using poly(A) selection, targeting the polyadenosine monophosphates at the 3′ tail of mature mRNA species, or depletion of rRNA by systems such as Ribo-Zero  and duplex-specific nuclease degradation.63 Recent developments include unique molecular identifiers (UMIs), molecular tags consisting of several random bases that can be used to detect and quantify unique transcripts.6365 Unwanted duplication of reads through amplification methods can readily be detected.67 The addition of UMIs significantly improves the accuracy of gene quantification, especially high expressing genes.69 The resulting library of cDNA molecules can then be assessed for quality before sequencing.To convert RNA into a library of molecules for sequencing, generally, it is first fragmented to an appropriate size for the chosen platform, either by physical or enzymatic approaches. First-strand complementary DNA (cDNA) is synthesized from the RNA sequences. Dependent on the platform and library kit used, platform-specific adapter sequences may be incorporated to the ends of the molecules to enable subsequent sequencing. Some systems add adapter sequences through ligation after cDNA synthesis ; other sequences may be attached to each molecule, including an inline index to identify the sample, allowing multiplexing of libraries when sequencing. Inline barcodes can be utilized to provide a label of origin for each RNA molecule.70 and single-cell RNA-seq (scRNA-seq).71 Strand-specific RNA-seq can resolve the ambiguity of overlapping genes transcribed on opposite strands, allowing identification of antisense expression by retaining information from which DNA strand the RNA was first transcribed.72 This information can be maintained using approaches that either incorporate a chemical modification in the second cDNA synthesis stage, with subsequent digestion of the nonmodified strand, or incorporate distinct primer adapters with the RNA.74 Library preparation protocols differ to achieve specific goals; TruSeqTM (Illumina) is a general method chosen when starting material is not restricted; Smart-Seq2 and Ovation  are suited to low input amounts.75For library preparation, one must consider how much RNA will be available for the experiment as well as the specific library types required, for example, those that maintain strand information or need significantly lower input RNA levels, such as those from FFPE or laser-captured micro-dissected samples,77 The Illumina short read ‘sequence-by-synthesis’ systems have been rapidly adopted by the research community due to high data throughput, accuracy, availability, and declining costs.High-throughput sequencing approaches are rapidly evolving regarding both technology and chemistry. Illumina, PacBio RS, Oxford Nanopore, and Ion Torrent are some of the most commonly utilized platforms.17 However, detection of lower abundance RNA species requires increased read sequencing, although RNA-seq shows a greater dynamic range than other assays.79 For the analysis of differential gene expression alone in human samples, 10–20 million reads per sample would provide significant information on most genes expressed. Investigation of alternatively spliced, novel isoforms, or fusion events, will require higher read number to capture the expression patterns, although increasing reads are associated with increased noise.17Sequencing depth, the number of fragments sequenced per sample, remains a critical factor for RNA-seq design. Studies have reported that increasing reads does not always provide increased biological significance.80With the depth of sequencing and library construction, comes the considerations of single-end reads or paired-end reads and read length. cDNA products may be sequenced from either single or both ends (paired). For simple differential expression analysis, single-end reads can provide valuable information. Paired-end reads, due to the size of RNA fragments produced , will provide more significant information as the number of reads from fragments spanning exon–intron boundaries will be higher. As RNA-seq investigates transcribed and processed RNA, it is crucial that a level of aligned reads or paired-end fragments span exon boundaries. Single-end reads can be utilized for analysis of the 3′ regions of transcripts, such as with Tag-seq and MACE, assuming expression as a whole from sequencing the end region only.81There are several biases in the analysis of RNA-seq differential expression: low-level transcripts producing high significance in expression-level differences and longer more abundant transcripts showing greater significance due to large number of reads per library aligned to their reference sequence. Read length is highly dependent on the application; for gene expression, profiling short reads  will detect the majority of RNA species in a library; for analysis of the transcriptome including identification of novel annotations, paired-end reads of 100+ bp will enable complete coverage of transcripts and novel splice sites; and for small RNA analysis, a read length of 50 bp would provide coverage of the majority of RNA due to their size. Long read sequencing is also possible using systems including PacBio and Oxford Nanopore, providing detailed analysis of specific isoforms expressed as well as allele-specific expression patterns, allowing the development of personalized transcriptomes.de novo transcript assembly is required. After alignment, feature counts are calculated and normalized, and differentially expressed features are identified. How these changes are biologically relevant to the experimental hypothesis is the final stage of investigation.Commonly, RNA-seq experiments investigating differential gene expression follow the stages outlined in 8285 Combination of approaches using different tools has led to improved results.86 Therefore, it is recommended to utilize multiple pipelines on the data set and understand fully the differences and similarities in the results. For this review, we will focus on several commonly cited, free, open-source tools to achieve differential expression analysis of human samples. The tools mentioned are not intended as an extensive list.There are an increasing number of tools and methods of analysis for RNA-seq data sets, with each stage of the study requiring appropriate quality control. Aside from command-line tools and cloud-based approaches, commercial products include CLC Genomics Workbench , DNAnexus, Ingenuity IPA (Qiagen), and Partek Genomics Suite. Software for differential expression analysis has been evaluated using both experimental and simulated data sets. Comprehensive reports of such tools have been presented previously.87 or FASTX-Toolkit.88 These provide graphical summaries of the sample reads, allowing quick visual identification of potential problems. Issues may commonly include over-represented sequences (e.g. adapter sequences or rRNA) or low-quality scoring bases at the 3′ end of reads. Tools to process the reads, filtering of poor bases, and trimming bases and adapters include Trimmomatic,89 Trim Galore,90 and cutadapt.91The Illumina sequencing platform will produce raw FASTQ files that represent the sequence of the library in question. FASTQ is a text-based file format including all the sequence data along with associated quality scores. Each Phred score represents a log-scaled estimated probability of error in the base being called, for example, a score of 30 indicates a 1 in 1000 probability that the base is incorrect. Initial processing of these read files should include quality assessment of the base calls using tools such as FASTQC92 Kallisto,93 and Sailfish.94 Alignment of reads requires software that can process mapping in a splice-aware manner.96 Many reads generated will span splice junction coordinates, and alignment will require algorithms to split reads to different exonic positions. Mapping software includes HISAT2,97 SOAPsplice,98 TopHat2,99 and STAR.100 These produce a sequence/binary alignment map (SAM/BAM101) file of the reads aligned to the genome. Alignments may be visualized using tools such as Integrative Genomics Viewer (IGV)103 or GenomeBrowse,104 providing an insight to read metrics at the feature level. One of the greatest challenges is the subsequent assignment of aligned reads to transcripts they originate from to infer gene expression. Several new generation tools have introduced alignment-free transcript or gene quantification methods.9294 These utilize k-mer-based matching to indexed transcript data sets, breaking reads into smaller k-mers, resulting in significantly faster analysis.94 A recent report, while confirming different pipeline performance was virtually identical for in vivo transcripts, demonstrated that alignment-based approaches were superior to alignment-free pipelines for total RNA analysis, as both small genes and low-expressed genes biased the accuracies of alignment-free approaches.105Post-processing of the cleaned FASTQ reads requires either alignment to the human genome, such as Ensembl GRCh38 or NCBI hg38 builds, or the associated human transcriptome or pseudo-alignment to the transcriptome and count modeling with tools such as Salmon,107 using tools such as Picard.108 There is significant discussion as to whether such reads should be removed from the analysis, as preferential amplification of cDNA fragments in the library preparation could result in a gene/isoform having an increased level of reported expression if such duplicated fragments were included.110 Other biases can consist of fragment GC-content, priming of reverse transcription by random hexamers, and rRNA depletion methods.112 A common practice to handle PCR duplicates would include removal of all but one representative read of identical sequences; however, this assumes that all identical reads were generated by PCR from the sample cDNA molecule.113 If removed, biologically significant information may be lost as smaller genes have reads that span the same genomic coordinates. UMIs enable tracking of fragments through library preparation, sequencing, and data analysis to overcome such biases.114Post alignment, processing of the data includes sorting by genomic coordinates and marking reads that can be assigned as optical or polymerase chain reaction (PCR) duplicates,85 Initial analysis of RNA distribution through technical replicates fitted well to a Poisson distribution, in which reads map to the transcriptome in a random unrelated fashion within the library.116 With decreasing cost and speed, the use of higher numbers of biological replicates demonstrated that sample variability was greater than the expected distribution, giving increased false positives. Subsequent methods to handle this variability include analysis based on negative and beta-negative binomial statistical models, such as edgeR,117 BaySeq,118 Cufflinks2,120 and DESeq2.121 Each requires an input of sample counts per gene or transcript that can be created with tools such as featureCounts122 or htseq-count.123 Raw counts generated are not suitable for comparison of expression levels. Transcript length and library read size are primary factors creating bias in such data. This high-dimensional count data are, therefore, fitted to the model and normalized by the chosen package. There are several metrics for normalization of gene expression, including RPKM (reads per kilobase per million mapped reads),124 FPKM (fragments per kilobase per million mapped reads), and TPM (transcripts per kilobase million).125 RPKM and related FPKM for paired-end sequences normalize gene coverage through correction of differing sample sequencing depth and RNA length. However, RPKM has been shown to be a poor metric for RNA abundance between samples.125 RPKM is calculated by dividing the read counts per feature with a scaling factor  and by the length of the gene (kb). Within the sample, the RPKM values can be assessed for comparative expression analysis; however, due to the nature of the variation of library sizes between samples, RPKM values will not be comparable, leading to confusion in the literature and the use of RPKM.125 TPM overcomes this by reordering the calculation, normalizing for gene length followed by normalization for library sequencing depth. Therefore, the sum of all TPMs in each sample will be the same, unlike RPKM/FPKM. This provides the opportunity for cross-sample expression-level comparisons. With the size of the dimensional data generated for gene counts, correction of statistical validity is required; a common approach is using false discovery rate (FDR) procedures to correct for multiple tests, for example, using the Benjamini–Hochberg method.126 Such processes are aimed at controlling the number of false positives when the null hypothesis has been incorrectly rejected.Expression levels of features, either at the gene or transcript level, are estimated from mapped read counts where appropriate feature annotation files exist. There is currently no consensus approach that is the most suitable to all situations, although this is an area of significant recent development.121 Cufflinks2,119 NOISeq,127 and edgeR.117 When working with transcript-level features, a further consideration would be the change in transcript length across samples/conditions that would alter intra-sample calculations.119 Comparative analyses of techniques used for differential expression studies have been reported129 and reviewed.131 Differential expression analysis results in lists of differentially expressed genes (DEGs) or features and associated fold changes. Decision on biological significance to filter the data set relative to fold change and adjusted p-value thresholds is highly dependent on the experimental design, which usually will require manual interactive inspection of the data. Principal component analysis (PCA) reduces the data dimensionality down into components of variation.132 By taking the main components and plotting them in either two- or three-dimensional (2D or 3D) space, samples can be visualized to enhance interpretability. PC1 describes the prominent variation within the data, PC2 the second, and so on. This aids visualization of groupings between replicates as well as potentially identifying sample outliers.One of the most commonly used protocols with RNA-seq data analysis is the assessment of changes in expression of genes between sample conditions. Several methods have been produced to normalize and model the count data produced from aligned short reads. Generally, the input for these tools will be raw counts, to avoid biases introduced through normalization. Methods to model expression from count data include DESeq2,133 that assemble reads into potential transcripts, using prior knowledge, but also will identify novel transcript isoforms, followed by comparison of expression levels. Alternative splicing occurs in 90–95% of genes in mammals; therefore, analysis of the alternative use of exons and splice sites from RNA-seq data is of vital importance.135 Tools to assess differential usage of features such as exons or splice sites handle the information differently, using an exon-based approach, comparing to the overall expression of the associated gene.137 Each has potential benefits and drawbacks, summarized in a recent report on the assessment of tools available.138Transcript-level differential expression analysis may assist in the detection of isoform changes. Transcripts can be assembled using tools including Cuffdiff2 or StringTie54 and ComBat.139 Numerous tools have been designed with batch effect correction stages optional and evaluated.55 Batch effects can ultimately range from increasing variability and reducing the power of an experiment, to becoming confounded with a desirable outcome and result in misleading biological interpretation.Batch effects can be a significant source of variation between batches of samples, resulting in reports of false DEGs. There are a number of approaches to correct for known or unknown batch effects, including surrogate variable analysis (SVA)140 and GenomeBrowse enable the user to view read alignments, highlighting read coverage and alternative splicing events with Sashimi plots, and summarizing the mapped read density over exons and junctions on the gene model.141 Combined with differential expression and differential usage data, display of individual genes of biological interest at the exon level can be used to assess potential complications from read alignment artifacts, for example, specific regions of the genome remain difficult to either sequence or align to with short read sequencing.143Visualization of RNA-seq data can be achieved in several ways, similar to other forms of high-throughput sequencing data. Genome browsers such as IGV, UCSC,144 QualiMap,145 and RNA-SeQC.146 This type of data visualization enables querying of read alignment efficiency as well as proportions mapped to features such as exons, introns, and splice sites.Data exploration throughout the analysis pipeline ensures precise results being reported. Useful tools for summarizing data from raw or processed sequence reads as well as alignment statistics visually include MultiQC,147 and (2) gene set enrichment analysis (GSEA), which utilizes the complete data set, ranking the entire transcriptome according to the expression-level changes using differing metrics.148 Both rely heavily on prior knowledge and functional assignment to genes through Gene Ontology terms and databases such as MSigDB.148 Specific tools have been created for such analysis, which invariably demonstrates gene length bias, where larger genes have a greater chance of showing significant changes. GOSeq, a Bioconductor package, aims to estimate and account for such bias.149 Analytical tools continue to develop; PathwaySplice addresses explicitly bias through accounting for number of exons/junctions and performs pathway enrichment analysis.150 Functional annotation data can also be readily queried using DAVID ,151 Panther ,152 QuickGO,153 and STRING.154 ClueGO, a Cytoscape app, enables rapid querying of ontology databases, producing clustered terms in a functional network.155 GSEA requires predefined collections of gene sets for analysis of the RNA-seq ranked list data set, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and BioCarta. GSEA provides a method for investigating changes in related sets of genes that may provide more insightful explanation than, for example, a large expression fold change of a single gene or numerous changes in genes with no biological theme. All genes detected experimentally are taken into consideration, not only those above the arbitrary cutoffs. Genes with small changes in expression that might not have reached the significance threshold may be of more biological importance within the same pathways, providing links between prior knowledge and newly generated experimental data.The biological significance of changes in the global transcriptome can be investigated through pathway enrichment of the list of DEGs/transcripts. Two example methods to aid functional significance assignment include (1) over-representation analysis (ORA), which compares the list of filtered DEGs against the annotated genome for over-represented functional assignment,156 LNCipedia,157 and NONCODE158 maintain information on specific classes of ncRNA.Novel genes, as well as noncoding RNAs (ncRNA) identified in RNA-seq data sets, can present a challenge for functional ontology assignment. Protein sequence homology can be readily assessed for protein-coding transcripts using current databases. While no standard functional annotation route is defined for ncRNAs, databases such as miRbase,z-score, the number of standard deviations from the mean expression of a gene, each sample’s expression can be represented through color variations. Hierarchical clustering, a way of arranging items in a hierarchy based upon similarity, can be used alongside heat maps to produce a dendrogram that shows the relationship between the rows . One-wa162 Even so far as versions and dates of databases utilized are critical to maintain the integrity of the results. There are diverse approaches to maintaining reusable and reproducible bioinformatics pipelines such as Subversion and Git . GitHub provides an open-source online resource for project tracking, sharing, and issue discussion. Code can also be created, shared, and annotated similarly with Jupyter scientific computing notebooks. Other options for reproducible analysis include AWS Elastic Cloud Computing,163 Docker,164 and Galaxy.165 Galaxy provides a web-based platform for high-throughput sequence data analysis. This platform is accessible to users without programming experience by providing a graphical web interface to command-line tools as well as predefined shared workflows and parameters. Tools and pipelines continue to develop rapidly with Galaxy adopting many of these improvements.167Throughout the analysis of any data set, it is critical to maintain reproducible workflows, providing detailed information on how data are manipulated, filtered, and assessed.168 Characterization of human diseases related to the eye can prove difficult due to the lack of high-quality human tissue required for the analysis; therefore, model systems, such as animals or cell-based, provide vital resources to further our understanding of eye development and disease. The role of noncoding and circular RNAs in eye disease has been the subject of a recent review.169 Here, follow some applications in ophthalmology and vision research.Vision research has benefited significantly from the use of RNA-seq over recent years.15 Further comprehensive analysis of eight normal eyes has been carried out, demonstrating transcriptome differences between macular and peripheral retina.170 Approximately, 80% of the annotated transcriptome was reported to be expressed in the retina, which showed significantly different alternative splicing patterns to the RPE, choroid, and sclera; hence, spatiotemporal gene localization is needed. Analysis of mature mRNAs and ncRNA such as long-intervening ncRNAs (lincRNAs) has been shown to be involved in numerous cellular pathways in development and disease. Analysis of total RNA within both fetal RPE and iPSC-derived RPE identified over 1000 lincRNAs and 180 novel genes expressed in fetal RPE. The research also confirmed that the transcriptomes of iPSC-RPE were comparable to fetal RPE, so enforcing the suitability of these cells for vision research.171Transcriptome analysis of three human donor adult healthy eyes has provided insight into which RNAs are expressed specifically in human retinal tissue. Farkas and colleagues identified 79,915 novel alternative splicing events that included 29,887 novel exons and 28,271 novel exon skipping events with 116 potential novel genes expressed in retina. The observations, while highly reproducible, indicate a high level of novelty in the makeup of the retinal transcriptome that highlights the difference between species and the importance of characterization of human tissue.in vivo methods of mRNA isolation from tissue and prefiltering of cells based upon morphology and function have been produced.172174 Post hoc PCA or hierarchical clustering of single-cell data has been relied upon to determine cell type classification. Recent scRNA-seq has identified up to 40 cell types of RGCs in the mouse retina using such approaches.175 While elucidation of model system RGCs has been invaluable, the need for further characterization of human cell types remains vital. To address this, human pluripotent stem–derived RGCs were profiled using scRNA-seq, showing a variable expression pattern of common RGC-associated genes, further indicating diversity within the cell population.176While global transcriptome analysis via RNA-seq has fueled our understanding of underlying mechanisms of disease, ultimately it provides little information on the basic unit of biology, the cell. Since the development of scRNA-seq using in-house approaches, the field has seen an increase in the number of commercial options available. Protocols generally involve tissue disruption, which can lead to changes in expression profiles, although 177 Using a rat model of progressive photoreceptor degeneration harboring a mutation in the Mertk gene , RNA-seq has been used to elucidate expression changes post stem-cell transplantation with human neural progenitor cells (hNPCs).178 Comparative analysis of gene expression profiles of treated and untreated RCS rats and controls identified 68 genes with altered expression patterns due to treatment with hNPCs. Pathway analysis revealed an enrichment of signaling involved in phagocytic response alongside the increase in photoreceptor cell survival. The underlying Mertk mutation causes improper phagocytosis of photoreceptor outer segments, and restoration of phagocytosis by hNPCs is encouraging. Similarly, mouse models of RP, including the rd10 mouse harboring a mutation in Pde6b, have been used to assess transcriptional changes underlying photoreceptor degeneration.39 Decreased expression of rod-specific genes was associated with a clear increase in Muller-specific gene expression, although other cell type-specific genes were dysregulated.39 Interestingly, alternative splicing of 284 genes was altered in the degenerated retina, with predominantly increased exon inclusion.Currently, mutations in over 75 genes can cause retinitis pigmentosa (RP), affecting the RPE and or photoreceptor cells, leading to progressive loss of vision. Stem cell–based therapies offer potential treatment avenues, either replacement of retinal cell types through differentiation protocols or protection via general neuronal lineage cells.179 AMD is characterized by RPE degeneration and consequent photoreceptor cell death. Although implicated in AMD, RPE phagocytosis has only recently been demonstrated to be dysfunctional by transcriptome analysis of RPE cells isolated from post-mortem AMD and normal age-matched control human eyes.180 To explore the disease progression, rat models of AMD were assessed for temporal changes in retinal transcriptomes. Enrichment ontology analysis has provided insight into cellular differentiation and developmental processes, all differential expression events were downregulated in comparison to controls. Gene clusters identified differing gene sets at the various disease stages linked to apoptosis.181 Targeted treatment of the exudative form of AMD through inhibition of vascular endothelial growth factor (VEGF) signaling using ascorbate-based targeted DNA hydroxymethylation has been validated via characterization of the resultant transcriptome in RPE cells  showing significant reduction in VEGF expression.182Understanding the pathogenesis of age-related macular degeneration (AMD) has been challenging due to the multifactorial etiology.183 Posterior polymorphous corneal dystrophy (PPCD) is a rare autosomal dominant disorder characterized by changes in Descemet membrane and the endothelial cell layer leading to decreased vision secondary to corneal edema.183 Although mutations in transcription factors OVOL2 (type 1) and ZEB1 (type 3) account for approximately 40% of all PPCD cases, the transcriptomes of PPCD endothelium and cultured human primary corneal endothelial cells were assessed to further elucidate potential biomarkers.185 Characterization of DEGs associated with ZEB1 and OVOL2 identified additional genes involved in proliferation, cell adhesion and migration, and cell morphology, which can be used to identify candidate genes for genetically unresolved patients.Corneal dystrophies are a group of genetic conditions that result in sight loss from various patterns of corneal opacity.186 To further understand the fibrotic response, RNA-seq has been used to identify dysregulated genes between primary fibrotic and nonfibrotic fibroblast cell lines isolated from glaucoma patients.187 Genes involved in inflammation and apoptosis were significantly upregulated in the fibrotic cell type, including RELB, PPP1R13L. MYOCD . PRG4 was upregulated in nonfibrotic cells and has been associated with high levels of hyaluronic acid and scar-less fetal wound healing.188 In total, 246 genes were differentially expressed in fibrotic cell lines compared to nonfibrotic, providing an insight to a distinct fibrosis gene signature.Success in glaucoma treatment can be determined by the level of fibrotic encapsulation post trabeculectomy surgery.189191 Elucidation of cellular transcriptomes, in tissue-related context, will provide an insight into the regulation of gene expression in assumed identical cell types. Combined with temporal experimental designs, analysis of thousands of cells at a time, using techniques such as DROP-seq and InDrops, can provide detailed analysis of cellular subgroups within systems of interest.193 Recent adaption of scRNA-seq has allowed the reconstruction of cell lineage histories in model systems.194198 Such large-scale informatics will drive knowledge of RNA expression through developmental stages and tissue types as well as providing the technology to approach many disease-related issues.RNA-seq is now becoming the standard method of transcriptome analysis as both the tools and technology continue to develop. Methods of analysis differ significantly and validation of results using different tools remains uncertain. As more comparative studies are evolving, more appropriate use of tools will be forthcoming. Continued development of RNA-seq technologies has resulted in the ability to analyze minimal amounts of starting material, even from older fixed and embedded archived tissue. Development of single-cell techniques continues to be a highly dynamic area of research.200 concomitantly collected RNA alongside the DNA samples. This initiative will result in increased diagnostic rates and the discovery of novel disease-causing variants, while also providing an extensive wealth of information on transcriptomes from individuals with varied genetic backgrounds. For eye disease, the transcriptome will provide insights into how genes alter the development or function of the eye and has the potential to provide researchers with novel targets for therapeutic strategies.With the availability of open-access sequence data in online repositories including NCBI SRA and EMBL ENA, combined with the increase in computing power, increasing the speed of pipeline analysis, the amount of knowledge to be gained from transcriptome analysis is increasing. Combined with other ‘omics data, RNA-seq analysis has the potential to link gene expression with genomic features such as epigenetic changes, DNA sequence alterations, and protein interactions. The Department of Health and Social Care’s 100,000 Genomes Project, whose aim was to sequence 75,000 genomes of patients with rare diseases and cancer,


Advances in neuroimaging have identified neural systems that contribute to clinical symptoms that occur across various psychiatric disorders. This transdiagnostic approach to understanding psychiatric illnesses may serve as a precise guide to identifying disease mechanisms and informing successful treatments. While this work is ongoing across multiple psychiatric disorders, in this article we emphasize recent findings pertaining to major depression in the elderly, or late-life depression (LLD), a common and debilitating neuropsychiatric illness. We discuss how neural functioning of three networks is linked to symptom presentation, illness course, and cognitive decline in LLD. These networks are (1) an executive control network responsible for complex cognitive processing, (2) a default mode network normally deactivated during cognitive demanding when individuals are at rest, and a (3) salience network relevant to attending to internal and external emotional and physiological sensations. We discuss how dysfunction in multiple networks contributes to common behavioral syndromes, and we present an overview of the cognitive control, default mode, and salience networks observed in LLD. Diagnostic and Statistical Manual of Mental Disorders (DSM), a discrete psychiatric disorder is diagnosed when the patient’s behavioral signs and symptoms exceed a binary diagnostic rule or cut-point. For example, a patient meets criteria for a DSM-V diagnosis of major depression when at least five of nine symptoms are present for two weeks or more and at least one of the symptoms is depressed mood or loss of interest/pleasure. However, the categorical approach is limited when we consider the arbitrary nature of diagnostic cut-points and the multi-determined origins of behavior2. Thus, diagnoses based only on presenting symptoms may be quite varied in regard to the underlying pathophysiology3. Major depression in the elderly, for example, has a multitude of possible psychosocial or biological etiologies (or both), including decreased social support, endocrine abnormalities, inflammatory processes, vascular disease, and neurodegenerative illnesses5.The practice of psychiatry has traditionally used a categorical approach to diagnosing psychiatric disorders. That is, in accord with the9. This transdiagnostic approach to understanding psychiatric illnesses may serve as a precise guide to identifying disease mechanisms. However, continued research is needed to better understand transdiagnostic neurobehavioral systems. While this work is ongoing across multiple psychiatric disorders, we emphasize recent findings pertaining to major depression in older adults or late-life depression (LLD) in this article. We do so because LLD not only is common and is associated with detrimental clinical outcomes such as dementia10, disability11, and mortality12 but also is challenging to treat. Thus, better understanding of the correlation between brain and behavior in LLD may inform understanding of the illness and identify new treatment targets. Below, we begin by providing an overview of the clinical significance of LLD. We then describe recent advances in the clinical correlates of brain-based networks in LLD.Novel dimensional conceptualizations of psychiatric illnesses have emerged to address the heterogeneity of traditional categorical diagnostic approaches. Thanks to advances in neuroimaging techniques, the fields of cognitive and affective neuroscience have helped identify neural systems that contribute to ubiquitous clinical symptoms across categorical psychiatric disorders5, and LLD increases the risk of dementia10, disability11, and mortality12. The risk of dementia is substantial in that LLD is associated with a four- to six-fold increase in the probability that Alzheimer’s disease or vascular dementia will be diagnosed10. The detrimental consequences associated with LLD highlight the importance of facilitating timely diagnosis and adequate treatment. Yet, over 50% of patients do not respond to initial pharmacological treatment, and only one third of older patients who receive cognitive behavioral therapy or an antidepressant achieve remission from depression13. Thus, novel depression treatments that can augment pharmacotherapy or be used independently in older adults with LLD are urgently needed. Neurobiologically informed treatment targets may hold promise in potentially altering the course of cognitive and affective symptoms in LLD as well as other psychiatric illnesses.LLD is a common and debilitating psychiatric disorder. Four percent of older women and three percent of older men have a current diagnosis of LLD16: (1) An executive control network (ECN), a functionally linked system made up of dorsolateral prefrontal, medial frontal, and lateral parietal cortices17, is involved in complex cognition, particularly in executive control18. (2) A default mode network (DMN), consisting of the medial prefrontal cortex, posterior cingulate cortex (PCC), inferior parietal cortex, and hippocampus, is normally deactivated during complex cognitive processing and active during internal mentation. (3) A salience/emotional processing network (SN), in which the right anterior insula and dorsal anterior cingulate cortex (dACC) are the primary hubs, assesses the significance of a stimulus and, together with amygdala activity, assigns emotional and motivational value to the stimulus.Neuroimaging has helped elucidate the brain regions and functions associated with psychiatric illnesses. Connectivity analyses from functional magnetic resonance imaging (fMRI) have been especially helpful. Functional connectivity (FC) refers to the phenomenon in which brain regions are activated and deactivated at the same time in the service of specific brain functions. A behavior or a clinical symptom typically involves synchronization of many brain regions in a network-based fashion. Evidence has identified three prominent functional networks in geriatric depression and other psychiatric illnesses16. For example, (1) decreased FC within the ECN is related to worse executive function (EF)19 and worse cognitive reappraisal ability20 or cognitive control over emotion21; (2) increased FC within limbic regions, namely the DMN and SN, is associated with greater depression severity23; (3) depression remitters exhibit increased FC within the ECN and decreased FC within DMN while non-remitters show no such changes24; (4) increased positive FC in the ECN-SN is related to worse EF and apathy in LLD25. Thus, interventions that enhance the efficiency and processing of the ECN and SN-DMN may be especially useful in the treatment of LLD.Understanding FC abnormalities between brain regions or networks can help clarify clinical symptoms associated with neural substrates and identify potential treatment targets in LLD and prove useful in monitoring cognitive decline. In general, pronounced symptoms of major depression in the elderly, such as executive dysfunction, rumination, apathy, and negativity bias, are associated with decreased activity in the ECN and hyperactivity in the SN and DMNNo single brain region is exclusively responsible for the heterogeneous symptoms of LLD. Instead, major depressive disorder likely arises when genetic and psychosocial predispositions drive structural and functional changes in multiple neural systems. Below, we have organized the subheadings of this review according to individual neural networks for ease of interpretation. Yet, as elaborated on below, it is clear that brain networks work in tandem to account for the diverse symptoms of LLD and other neuropsychiatric illnesses. Furthermore, although the primary emphasis of this review is on fMRI, multiple brain imaging techniques are increasingly used to understand the inner workings of disease states. The literature relevant to LLD and cognitive aging is noted below.26. Clinically, disruption of the ECN results in executive dysfunction, including susceptibility to distraction, an inability to sustain attention, poor multi-tasking, organizational difficulties, and concrete or rigid thinking27. Executive dysfunction is common in LLD. About 30 to 40% of non-demented older adults with LLD demonstrate impairments in EFs on neuropsychological examination28. Depressed older adults often perform poorly on tests of word-list generation, cognitive flexibility and problem solving, planning, and susceptibility to interference or distraction29 and performance on these tests correlates with brain abnormalities in prefrontal, medial frontal, and parietal cortices30. Impaired executive control also predicts a poor illness course in LLD31. Deficits in word-list generation and response inhibition predict poor and slow antidepressant response, relapse, and greater levels of functional disability34.Disruption of the ECN is prominent in LLDet al.35 compared the effects of age and illness on cognitive processing by contrasting network activity in LLD (age of 65 or more) to age-matched non-depressed controls and young adults with major depression (age range of 18 to 33). During a cognitive control task within MRI, older adults with LLD exhibited increased activity in several ECN regions, as well as areas outside the ECN, compared with both age-matched controls and younger depression subjects. These findings suggested that areas of increased neural activation were due not to age or depression alone but to a combination of the two. Thus, Raoet al.35 propose that ECN dysfunction in LLD might represent a type of accelerated aging process. This is consistent with the notion that depression precedes neurodegeneration and cognitive decline or places older adults at increased risk of those conditions. Also in keeping with this idea is the fact that although both younger and older adults with major depression exhibit greater cognitive impairments compared with their respective non-depressed age-matched peers, the magnitude of impairment is typically greater in the older adults36.Depression and increasing age appear to exacerbate dysfunction of the ECN in LLD. Raoet al.37 recently found that depression severity in 79 older adults with LLD was positively correlated with FC between the left dorsolateral prefrontal cortex (DLPFC) and other bilateral frontal regions (including the dACC). Furthermore,greater left DLPFC-dACC FC was associated with worse cognitive performance in LLD subjects, particularly in memory. Although these findings are consistent with a prior study by Alexopouloset al.15 documenting an association between ECN connectivity and executive dysfunction in LLD, findings from that study revealed thatdecreased FC of the ECN was associated with worse cognitive performance. Moreover, compared with remitters, non-remitters in the study by Alexopouloset al. demonstrated decreased FC at baseline between key ECN regions, including the bilateral DLPFC, dACC, and inferior parietal cortices15. Therefore, the association between worse cognitive performance andgreater left DLPFC-dACC FC might initially appear counterintuitive. To help explain these findings, Gandelmanet al. posit an interesting hypothesis whereby a bias toward negatively valenced stimuli may direct (increased) attention away from cognitive tasks at hand 37. This novel hypothesis remains to be tested. Apparent FC irregularities in LLD may also simply reflect alternative ways in which patients process information. For example, Weisenbachet al. found that older adults with LLD exhibitedgreater activation in the left inferior frontal gyrus of the ECN during a word-list learning task when compared with non-depressed older adults, although there were no task behavioral differences between groups38.Functional neuroimaging findings illustrate the role of the ECN in LLD. Using a seed-based correlation approach to explore resting-state FC (rsFC), Gandelmand = 0.22)39. However, cognitive training seems to work best in non-demented elderly patients with some degree of cognitive impairment, which is why it is especially promising to older adults with major depression. For example, a meta-analysis of 17 randomized controlled trials of computerized cognitive remediation in mild cognitive impairment found a moderate effect on multiple cognitive domains (g = 0.35), including EF and memory40, and the largest treatment-related cognitive gains in a small sample of LLD patients followed 4 to 6 weeks were found in those with the worst EF prior to treatment41.Non-pharmacological attempts to rehabilitate executive dysfunction and the ECN have become more common in older adults with or without depression. Computerized cognitive remediation is a treatment that has been used to improve cognition in the elderly. It is a behavioral intervention that uses controlled repetitive learning to improve cognitive functioning. A meta-analysis of 51 randomized controlled trials of computerized cognitive remediation in cognitively normal elderly patients indicates that cognitive training programs have a modest effect on global cognitive functioning . Further investigation has found that DMN activity normally increases not during rest per se but during internal processes, such as perspective taking  and in thinking about the past  or the future46.The DMN was originally described by Shulman47. Failure to reduce DMN activity might be a sign of an inability to quiet or inhibit internal mentation or emotional processing48. This happens during both routine and complex activities. For example, Shelineet al.48 found that depressed patients were less likely than non-depressed controls to decrease activity within the DMN when simply viewing negative pictures. Moreover, the DMN regions of the anterior cingulate and ventromedial prefrontal cortex failed to decrease in activity when depressed patients (but not non-depressed controls) attempted to reappraise  negative pictures. Increased DMN activity is therefore associated with an inability to regulate emotions in major depression. Furthermore, the strength of the FC correlation between “top-down” brain control regions  and “bottom-up” DMN/SN emotional centers is related to greater depression severity in LLD25.Default mode activity is thought to be negatively correlated with activity within the ECN. The need to reduce the internal processes of the brain in order to focus on the  task at hand might be reflected by a reduction of DMN activity during effortful cognitive processing49. One hypothesis is that DMN over-activity in anxious depression during cognitive demanding tasks could represent internal mentation interference 49, consistent with evidence that greater anxiety in older adults is associated with decreased connectivity between anterior and posterior hubs of the DMN50. Alternatively, increased activation in posterior brain regions in anxious depression might represent heightened alertness to a threat in the environment22.Hyperactivity of the DMN may help drive the co-occurrence of anxiety in LLD. It was found that, when completing an MRI-based test of cognitive control, older adults with LLD and high anxiety, compared with older adults with LLD alone, experienced greater activation in the anterior and posterior regions of the DMN51. Older adults with mild cognitive impairment, a clinical risk state for dementia, show lower FC in the PCC and medial temporal regions of the DMN, brain regions implicated in memory52. In patients with actual dementia (Alzheimer’s disease), decreased activation within the DMN during rest and tasks is observed54, and some authors have found that reduced deactivation is related to increased amyloid burden, a presumptive biological marker of Alzheimer’s disease56. Notably, activation patterns within the DMN vary by Alzheimer’s disease stage. Findings from patients with early Alzheimer’s disease revealed hyperconnectivity in anterior medial frontal DMN regions and hypoconnectivity in posterior DMN . Connectivity in both anterior and posterior DMN eventually decreases as dementia progresses57. Some have speculated that anterior DMN over-activity in young adulthood occurs in the context of a physiological process that predisposes individuals to dementia in later life58. This is clinically relevant to LLD, where increased DMN FC with the striatum (a reward center) is observed in patients who fail to respond to antidepressants when compared with patient responders59. Furthermore, it has been found that DMN over-activity persists in some LLD patients compared with non-depressed controls even following patient remission from acute-state depression60. Thus, DMN hyper-activity following depression treatment may be an indicator of later cognitive decline in LLD. This association between DMN over-activity and cognitive decline in LLD remains to be explored.Activity changes within the DMN occur with age and appear to precede cognitive decline. Lower baseline rsFC in the ECN, DMN, and SN is associated with cognitive decline in healthy elderly patients, even when structural volume loss is controlled for23. Notably, in a coordinate-based meta-analysis of resting-state fMRI, the patients with amnestic mild cognitive impairment also show decreased ALFF in the PCC cortex61. More research is needed to understand the biological overlap among patients with LLD and pre-dementia states.Besides FC, other fMRI analytic methods may help distinguish abnormal brain activity in LLD and mild cognitive impairment/pre-dementia states. Functional segregation  methods like regional homogeneity and amplitude of low-frequency fluctuation (ALFF) may be particularly helpful in deciphering the exact network node origin of abnormality. For example, compared with never-depressed older adults, older adults with LLD showed significantly increased ALFF in the middle temporal cortex, insula, fusiform gyrus, and cerebellum and significantly reduced ALFF in the inferior parietal cortex, mid-cingulate, and PCC/precuneus62. The right anterior insula is one of the primary SN nodes63. Insula and amygdala activation can represent the value of emotional stimuli presented to the individual17. The SN has prominent connections to other limbic areas and several subcortical structures . The SN is a bottom-up processor that recruits other large-scale networks when responding to the salient stimulus62. For example, when attempting complex cognitive processing, the SN is thought to disengage the DMN and engage the ECN64. Thus, at rest and in the absence of internal and external stimuli, FC between the SN and the DMN and ECN should typically be negatively correlated. Greater dissociations between the ECN and the SN correlate with better cognitive task performance65, consistent with the ability to focus attention and ignore internal  and external  distraction. This standard pattern of connectivity is disrupted in major depression66. Older adults with LLD exhibit decreased negative FC between the ECN and the SN when compared with non-depressed controls67. Furthermore, reduced negative FC between the ECN and the SN in LLD correlates with worse cognition67, greater depression severity67, and worse treatment response to antidepressants68. Wanget al.25 contrasted correlation patterns among the major neural networks in patients with LLD and non-depressed age-matched controls. Of note, the ECN and SN were correlated with an affective/default mode network, but this association was observed in non-depressed elderly patients only. No significant correlation was observed among elderly depressed subjects. These findings could represent a failure of inter-network cohesiveness in LLD.The SN is important in the relationship of emotion to visceral function, allocation of attention, and orchestrating networks upon stimulus presentation16. Findings from that study also revealed increased FC of the right insula to the right PCC in older adults with LLD and high apathy when compared with non-apathetic depressed elderly patients16. Elsewhere, volumetric analyses reveal that older adults with major depression who exhibit prominent melancholia and vegetative symptoms have lower basal ganglia volumes compared with LLD patients without prominent melancholia and vegetative symptoms69. Overall, one interpretation of these findings is that increased FC between SN and DMN may predispose individuals to depression, core features of which may include increased apathy and difficulty in engaging in cognitively demanding tasks while ignoring irrelevant, negative stimuli16.Dysfunction to the SN is correlated with vegetative symptoms in LLD. Findings from one study found that, relative to LLD patients without apathy, LLD patients with high apathy had decreased rsFC of the right anterior insula with the right ACC, basal ganglia, and bilateral posterior parietal cortex70, those carrying the apolipoprotein e4 risk allele for Alzheimer’s disease71, patients with mild cognitive impairment72, and individuals with clinically defined Alzheimer’s disease73. Similar to the DMN FC, SN FC changes as a consequence of disease progression. Schultzet al.74 recently conducted a study of 91 older adults who underwent resting-state fMRI and both amyloid (C Pittsburg compound B) and tau (AV1451) positron emission tomography (PET) imaging. Notably, increased connectivity in both the SN and DMN was associated with elevated amyloid imaging among participants with little evidence of tau. However, as both tau and amyloid levels increased, patients experienced decreased connectivity in the SN and DMN. These findings highlight the point that connectivity changes in preclinical dementia are not limited to the DMN. Given that depression severity is correlated with both DMN and SN functioning in LLD23, severe depression may signal an intrinsic network dysfunction that heralds the clinical manifestation of cognitive impairment or the onset of eventual cognitive decline.Increasing evidence also points to the importance of the SN in pre-dementia states. Increased within-SN FC has been found in cognitively normal individuals with elevated amyloid levelset al.76 reported that varying patterns of abnormal FC differentiated four neurophysiological subtypes in middle-aged patients with major depression. Two subtypes demonstrated greatly reduced FC in DMN and SN, and two subtypes demonstrated reduced FC in ECN and DMN regions. Patients with reduced DMN-SN FC exhibited more vegetative symptoms, whereas patients who exhibited reduced ECN-DMN connectivity had greater anxiety. Although similar analyses have yet to be conducted with LLD, Liet al. found that different symptoms correlated with distinct inter-network connectivity patterns determined within independent components analysis67.Neural functions contribute to the complex symptom presentation observed in LLD. Above, we described the large-scale neural networks that contribute to clinical symptoms in LLD. A growing literature indicates that decreased activity in the ECN and hyperactivity in the SN and DMN are associated with some of the most prominent symptoms in LLD . Furthermore, recent evidence suggests that resting-state network activity can distinguish several neural endophenotypes of major depression with distinct clinical symptom profiles. DrysdaleThe traditional clinical diagnostic entity of major depression very likely represents several diseases and distinct syndromes. It is plausible that at least some of the contradictory fMRI findings in the literature on LLD or major depressive disorder  occur as a result of patient cohorts being recruited and lumped together as one disorder based upon traditional descriptive diagnostic systems. This underlies the importance of disentangling the neuroanatomical substrates of clinical symptoms in LLD. If the studies mentioned above are used as a foundation, neurobiologically informed subtypes of LLD may soon serve as precise guides to identifying disease mechanisms. Furthermore, together with other imaging modalities , fMRI may be especially useful in clarifying the overlap between LLD and neurodegenerative disorders.


The ability to profile hundreds of thousands to millions of single cells using scRNA-sequencing has revolutionized the fields of cell and developmental biology, providing incredible insights into the diversity of forms and functions of cell types across many species. These technologies hold the promise of developing detailed cell type phylogenies which can describe the evolutionary and developmental relationships between cell types across species. This will require sampling of many species and taxa using single-cell transcriptomics, and methods to classify cell type homologies and diversifications. Many tools currently exist for analyzing single cell data and identifying cell types. However, cross-species comparisons are complicated by many biological and technical factors. These factors include batch effects common to deep-sequencing approaches, well known evolutionary relationships between orthologous and paralogous genes, and less well-understood evolutionary forces shaping transcriptome variation between species. In this review, I discuss recent developments in computational methods for the comparison of single-cell-omic data across species. These approaches have the potential to provide invaluable insight into how evolutionary forces act at the level of the cell and will further our understanding of the evolutionary origins of animal and cellular diversity. Single-cell RNA sequencing has become a powerful and popular tool, yielding rich and informative cell-type atlases of many tissues, and even whole organisms . These ede facto method for categorizing cells, because it is convenient, easily measured, and comparable across models and systems. With recent advances in sequencing, microfluidics, and nano technologies, it is also now possible to profile the transcriptomes of thousands or even millions of cells in a single experiment  but preserves intra-dataset heterogeneity . CombineC), the specificity index  of a gene (g) for a cell type (c) is defined as the ratio between the level of expression of g within c (gc) and the mean expression of g across C  can unbiasedly assign cluster matches across datasets . This haEven assuming clusters are correctly matched across datasets, comparative analysis of cell transcriptomes remains a difficult task due to batch effects . ComputamnnCorrect/fastMNN, identifies Mutual Nearest Neighbors (MNNs) in high-dimensional gene expression space to identify cell type specific batch-correction vectors  (mnnCorrect/fastMNN (The R toolkit egration . The oriis (CCA) . CCA ideis (CCA) . In Seur/fastMNN .Seurat and mnnCorrect/fastMNN reduces this effect when cell types are present in only a subset of the datasets, because they will not have a mutual nearest neighbor in any other dataset. The panoramic stitching algorithms of Scanorama use a more generalized MNN technique, and aim to even further reduce the amount of overfitting between datasets, using a process that is similar to the creation of panoramas from individual images  to learn shared and unique gene expression signatures between datasets   .Harmony corrects analogous cell types from different datasets toward a shared centroid in low-dimensional PCA space, running iteratively until the datasets converge . Alternatively, removing the most highly correlated genes during clustering analysis may also be a prudent approach  or result from convergence onto the same cellular identity (homoplasy). The re-use, re-purposing, or co-option of homologous cellular modules and gene regulatory networks is thought to underlie cell type convergence . Such deFinally, it will be necessary to incorporate phylogenetic comparative methods when comparing differences between species in regard to cell types and gene expression patterns. Biological traits show dependence across species due to the evolutionary history of those species – with more closely related species sharing more similar traits. This should also apply to cell type identities and gene expression patterns . Phylogein silico or using CRISPR barcodes (Many techniques, tools, and technologies for single-cell sequencing are already applicable for comparisons across species. However, improvement and refinement of current approaches based on evolutionary knowledge should be considered a priority for the field of transcriptomics and evolutionary cell biology. Understanding the evolutionary history and relationships between cells will provide insight into definitions of cell types, and the molecular mechanisms that govern their identities. Using this evolutionary framework, examining the continuum between developmental stage, cell states, and cell types may even elucidate how cell types evolve . A holisbarcodes . IncorpoMS conceived and wrote the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The emergence of drug resistance depends on the ability of the genome of cancer cells to constantly mutate and evolve under selective pressures. The generation of new mutations is accelerated when genes involved in DNA repair pathways are altered. Notably, although the emergence of new mutations fosters drug resistance, new variants can nevertheless become novel antigens that promote immune surveillance and even restrict cancer growth. The ability of tumors to adapt to drug pressure depends on the capacity of cancer cells to evolve over time. These observations raise the important issue of how treatment plans can overcome the near-certainty of disease relapse. Until recently, much of the effort has been directed at preventing or restricting tumor evolution. However, understanding the ways tumors mutate and how this affects immune surveillance and immune responses could be a more effective approach for cancer therapies.Tumors evolve owing to their inherent genetic instability and molecular heterogeneity. Cancers encompass different cellular populations carrying distinct genetic and epigenetic alterations and are capable of continuously acquiring new somatic variants. Several lines of evidence indicate that tumors deficient in mismatch repair (MMR)—characterized by hypermutability and an increased mutation rate—are highly responsive to immune-checkpoint inhibitors , 2. ThisRizvi and colleagues have reported how a higher non-synonymous mutation burden is associated with improved response, durable clinical benefit, and progression-free survival in two independent cohorts of non-small-cell lung cancer patients . In theiHow MMR deficiency affects the response to immunotherapy has been unraveled in recent work demonstrating that tumors containing a high number of somatic mutations, owing to defects in DNA MMR, are sensitive to immune-checkpoint blockade—with anti-PD-1 antibodies—across 12 different tumor types [Overall these studies underline the concept that a high mutational burden and elevated number of neoantigens, due to alterations in MMR genes, renders tumors responsive to immunotherapy regardless of the type of cancer. Based on these lines of evidence, the FDA granted approval to the anti-PD-1 antibody pembrolizumab in advanced solid tumors in patients whose cancer is DNA-MMR deficient . This isAlthough immunotherapy has shown promising results, unfortunately it is effective in only a minority of cancer patients, and thus there is an intense interest towards understanding why immunotherapeutic approaches might differentially benefit distinct patient subgroups.+, CD4+, and myeloid cells as well as a complex network of pro-inflammatory cytokines [A major limitation of the efficacy of immunotherapy is represented by the so-called ‘immunologically cold tumors’. This term usually refers to there being limited, or no, immune response within the tumor tissue. Cold tumors are those that are not recognized by the innate or adaptive immune system, and do not elicit strong immune responses. Different cold immune profiles have been identified by analysis of histological samples. The ‘immune-excluded’ phenotype is characterized by the presence of abundant immune cells that, however, do not infiltrate the parenchyma but instead remain in the stroma of the tumor mass . A seconytokines . Such stSeveral strategies have been considered to increase immune surveillance of cold tumors. For example, the impact of MMR deficiency on cancer immune surveillance was assessed recently by using syngeneic mouse models . SpecifiCombined, these results suggest that a forced increase in the mutational burden (particularly the levels of frameshifts) could, paradoxically, be beneficial. As a follow-up, colorectal cancer mouse cell lines were treated with temozolomide (TMZ), a chemotherapeutic drug that triggers DNA damage, and TMZ-resistant cells were injected into syngeneic mice . A subseAnother approach to increase the response of cancers to immunotherapies is delivery of oncolytic viruses. For example, a small phase Ib clinical trial recently tested the effect of combining an oncolytic virus with the anti-PD-1 pembrolizumab in a cohort of advanced melanoma patients. This combination reported an impressive 62% overall response rate, with 33% being complete responses . The str+ T lymphocytes that specifically recognized KRAS harboring the G12D mutation in a patient with multiple metastatic sites. After the lymphocytes were expanded and infused, an objective regression was observed for all seven lung metastases [A further option is represented by a personalized cancer treatment vaccine that targets patient-specific neoantigens. Ott and colleagues recently showed how this approach could stimulate an effective anti-tumor response in melanoma patients. When the vaccine was injected into the patients, it drove immune T-cell responses that recognized neoantigens on tumor cells, resulting in complete responses . Oncogentastases .Other strategies to convert an immune-cold tumor into an immune-hot target include the use of nanoparticles capable of delivering immune-stimulating drugs into tumors and, in turn, stimulating T-cell invasion. Reprogramming the tumor microenvironment to elicit T-cell activation and enhance tumor immunity represents another way to effectively convert cold tumors into hot tumors. Finally, small-molecule drugs that increase T-cell activity could act synergistically with immune-checkpoint inhibitors.Up until now, significant efforts have been made in the direction of preventing and/or restricting tumor evolution for therapeutic strategies. An alternative approach is to increase the immunogenicity of cancer cells, thus fostering immune surveillance. In this regard, several strategies have been proposed, including, first, inactivation of DNA repair to increase neoantigen levels in cancer cells; second, modification of the tumor microenvironment; and finally delivering tumor-specific viruses. These approaches are being tested in preclinical models or in early-stage clinical experimentation, with the ultimate goal of improving immune surveillance and restricting cancer growth.