Cancers exhibit functional and structural diversity in distinct patients. In this mass, normal and malignant cells create tumor microenvironment that is heterogeneous among patients. A residue from primary tumors leaks into the bloodstream as cell clusters and single cells, providing clues about disease progression and therapeutic response. The complexity of these hierarchical microenvironments needs to be elucidated. Although tumors comprise ample cell types, the standard clinical technique is still the histology that is limited to a single marker. Multiplexed imaging technologies open new directions in pathology. Spatially resolved proteomic, genomic, and metabolic profiles of human cancers are now possible at the single-cell level. This perspective discusses spatial bioimaging methods to decipher the cascade of microenvironments in solid and liquid biopsies. A unique synthesis of top-down and bottom-up analysis methods is presented. Spatial multi-omics profiles can be tailored to precision oncology through artificial intelligence. Data-driven patient profiling enables personalized medicine and beyond. Tumor development is dynamic and evolutionary. To adapt to environmental changes in local cancerous tissues within a timed progression window, tumors acquire spatial and temporal heterogeneity2. As a result of this structural and functional complexity of tumors, cancer therapies exhibit variable responses in distinct patients and cancer types. Conventional chemotherapies are prone to fail due to drug resistance. Tumor origin may be associated with foreign infections, genetic causes, cellular diseases, evolutionary formation, and systemic perturbation of homeostasis3. As one of these mechanisms for tumor initiation, a cancer cell can be considered as a cancer stem cell, due to its similar features in self-renewal and differentiation of normal stem cells4. In the cancer stem cell model, therapeutic reagents may eliminate cancer cells with limited proliferative potential but remain unsuccessful to target multipotent cancerous cells. Cancer relapses after chemotherapies in a majority portion of patients, because the resistance of cancer stem cell to chemotherapies is a primary reason to relapses5. Reprogramming of tumor cells during drug treatments may explain drug resistance that leads to relapses6. Rare cells develop unexpected epigenetic programs to acquire secondary mutations for stable resistance. Secondary genetic alterations and proteomic bypass mechanisms contribute to the resistance7. Therefore, cancer heterogeneity and therapeutic variability indicate the need for personalized medicine, wherein precision treatments are designed based on an individual’s functional molecular profiles8.Tumors arise from abnormal cells that acquire uncontrolled proliferation and extensive differentiation abilities9. Tumor genotypes are used for therapies in hematologic and solid tumors. The current medical practice focuses on single lesions, wherein invasive tumor biopsies either from the bone marrow or from affected nodal/soft tissue are targeted. However, the single-site tumor biopsies fail to identify the entire mutational profile due to the limited genomic heterogeneity of an individual’s disease. Solid biopsies also cause biases in disease characterization and lead to erroneous therapeutic decisions due to the difference in sampling locations within biopsies. In the meantime, circulating free DNA (cfDNA) has been widely explored since its identification in 194810. Cancer patients have increased levels of DNA fragments in the blood plasma frequently, which are possibly released from apoptotic or necrotic cells11. Therefore, circulating tumor DNA (ctDNA) shows the potential to represent genomic biopsy. Compared with single-lesion tissue biopsies, liquid biopsies exhibit better performance to elucidate acquired resistance. Next-generation sequencing (NGS) has enabled profiling of ctDNA as a small fraction of total cfDNA, opening new doors to use of liquid biopsies for disease diagnostics13.Personalized medicine benefits from precise molecular profiles of tumors in the form of solid and liquid tumors. Solid tumors are composed of immobile cells, such as epithelial or mesenchymal cells that accumulate multiple mutations. On the other hand, liquid tumors contain mobile and invasive neoplastic cells with less number of mutations14. In addition to PD-L1, the circulating T cells with different T-cell receptors show the potential to be unique biomarkers for immuno-oncology14. The heterogeneity presented by both CTCs and immune profiles, and the limited number of CTCs emphasize the need for effective biomarker-based detection methods, thus developing automated, multiplex imaging methods are promising15.However, the lower quantity of ctDNA in cfDNA limits the sensitivity of detection and imaging is not the optimum approach for measuring DNA due to low signal levels. In addition to the histological analysis of solid tumors, imaging circulating tumor cells (CTCs), CTC clusters, and immune cells is an alternative way to analyze the tumor’s molecular compositions. CTCs are considered as real-time “liquid biopsy.” Both single CTCs and CTC clusters present heterogeneous molecular characteristics. Also, CTCs in liquid biopsies give a better representation of dynamic immune profiles, such as PD-L1 expression, than tissue biopsies16. These complementary schemes elucidate the complexity and heterogeneity of tumor microenvironments. Spatial molecular analysis of RNA and protein markers has great potential to dissect cellular constituents and interactions in tumor microenvironments at the tissue level, cell clusters, and subcellular scales. These high-dimensional cellular image-omics profiles may show differences between biopsies from the patients who either respond or resist to therapies. Spatial maps of patient subgroups can be used as training and learning datasets. Artificial intelligence platforms can then predict a new patient’s response to drugs for personalized treatment plans based on the spatial data collected from both top-down and bottom-up approaches. This integrative framework impacts precision medicine and therapeutic design.This perspective focuses on the applications of bioimaging technologies to screen patients’ molecular heterogeneity in solid tumors and cellular constituents of liquid biopsies for predictive and personalized medicine applications Fig. . Two mai17. Although positron emission tomography (PET) measures the biochemical activity of cancer cells, it is limited to a few traces that depend on cellular metabolism and that is insufficient to understand the entire molecular profiles of human cancers18. Thus, emerging solutions are urgently needed for technological advances in cancer diagnostics for the simultaneous detection of hundreds of biological markers in tumors to decipher a wide range of molecular drivers in human diseases.Molecular imaging is a ubiquitous and powerful approach to rapidly monitor the progression of cancers at low cost. Several imaging modalities are used by clinicians to detect, diagnose, and stage human cancers including X-ray, ultrasound, and magnetic resonance imaging (MRI). However, these imaging techniques detect anatomical aberrations at the organ level without specific genetic inference and with limited molecular informationMolecular profiles of cancers aid clinicians in designing personalized treatment regimens and potentially open doors to drug discovery research. Complementary imaging methods in histological and pathological routines utilize specific protein-targeting chemistry such as antibodies to detect molecular profiles at the cellular level. Although these microscale imaging efforts have identified vital biomarkers in cancers, they are constrained by the number of markers that can be measured in a biopsy sample. Recent advances utilize multiplex cellular imaging systems that allow visualization of a larger number of markers of up to 50 unique targets. Current research aims to develop automated, multiplexed platforms to detect numerous markers and to feed the information in an algorithm to diagnose patients and to predict their response to therapies.19. The tumor microenvironment holds a wealth of information about the biochemical, physical, genetic aberrations in cancers that can be used to design patient-specific therapies. This information often remains unlocked due to the limitation of the commonly used technologies. For example, routine lab assays often fail in maintaining intact specimens of tissue samples or in detecting multiple markers from one sample. Although sequencing and flow cytometry have been informative about tumor microenvironment (TME), spatially resolved profiles of genes and molecular products such as RNAs and proteins are critical to uncovering molecular drivers of cellular interactions in tumors , which are a fraction of CTCs that resides in distant sites23. These CTC clusters provide information about the tumor’s molecular evolution, their release mechanisms, and location from which they are coming on primary tumors  system detected 32 proteins and phosphorylation sites in formalin-fixed, paraffin-embedded (FFPE) tissue samples from 20 breast cancer patients. Several protein markers were uniquely distributed in their subcellular volumes at different sites of the cancer masses, such as the invasive front, center, and the periphery. The relation between two mRNA transcripts was also explored in subcellular regions that are commonly used as treatment targets (CK19 and HER2) and their corresponding proteins26.CTCs and immune cells circulate in the bloodstream under the effect of serum and other growth factors. When treated with drugs, these single cells receive messages from the suspension-form micro-niches to respond or resist. Thus, the “subcellular microenvironment” in single cells from liquid biopsies can be profiled for functional analysis. Receptor interactions, signaling networks, and chromatin/protein interactions contribute to these subcellular and molecular microenvironments. Spatially resolved methods achieve a subcellular resolution to assay intracellular molecular distributions. For example, multiplexed IMC detected 16 protein markers associated with phenotypes of CTCs, DTCs, and immune cells from liquid biopsies (blood and bone marrow) from a metastatic prostate cancer patient Fig. . In this27. Ten markers were combined in an RNA panel for identifying CTCs that are functional in metastatic breast cancer patients. In addition, super-resolution imaging of chromatin state28 and spatially resolving protein modifications29 in the epigenetic mechanisms further sheds light on subcellular microenvironments  was used to remove the signal from the prior cycle, re-labeling of another set of antibodies, followed by imaging and another signal bleaching32. This process of imaging, bleaching, and re-labeling was then repeated for multiplexed detection. CO-Detection by indEXing (CODEX) was then designed as another sequential labeling method that utilized and rendered dye-labeled DNA sequences that were conjugated to a library of antibodies. CODEX rendering process was repeated up to 15 cycles to anneal and strip DNA barcodes for multiplexing proteins in spleen samples33. A recent antibody–DNA barcoding technology, Immuno-SABER , was combined with a unique signal amplification method for multiplex imaging of lowly expressed markers in tissues30. Fluorescence-based multiplexing methods are available for individual laboratories using automated microfluidic and imaging instruments.Multiplex imaging techniques meet TME studies with their measurement capabilities in native tissues with a large target panel, as summarized in Table 34. This platform has recently been extended to the spatial mapping of proteins by IMC35. In this method, laser ablation was used to raster scan the tissue sample, releasing vapors from specimens to be analyzed by a TOF detector for identifying mass-to-charge ratios  and abundances of isotopes. As a complementary effort, the second approach is multiplexed ion beam imaging by the TOF (MIBI-TOF). In this technique, an ion beam source was used to raster scan the tissues to generate secondary ions that are detected by a TOF analyzer. MIBI-TOF profiled 36 proteins in triple-negative breast cancer (TNBC) FFPE samples from 41 patients. In this work, a structured view of immune cell populations and checkpoints expression was observed and overall survival was linked to immune profiles in TNBCs36.Another direction for multiplexing proteins is by imaging of mass-tag labeled antibodies. Isotope labels have created high-dimensional profiles of cellular suspensions from biopsies to decipher cellular regulations by a mass cytometry technique (Cytometry by time-of-flight (TOF)) at the single-cell level38 has profiled genome-wide transcriptional changes in tissues at sub-100 µm resolution. In the ST platform, a barcoded substrate was prepared and the tissue sample was digested directly on this interface. The resultant cDNA sequences were analyzed by a sequencer. ST was applied to prostate cancers from a high-throughput sample size of 6750 tissue regions, revealing the spatial gradient of stromal cells to tumor regions39. The integration of bead chemistry with sequencing has allowed higher resolution mapping of spatially resolved RNA maps. Slide-seq utilized a relatively simplified protocol with 10-µm resolution in the mouse brain and other organs40. Another implementation, high-definition ST (HDST), created spatially resolved maps of histological sections at 2 µm resolution41. Different enzymatic chemistry was also used to map transcriptome using cDNA conversion based on fluorescent in situ RNA sequencing (FISSEQ) technology42. Recent work introduced a reconstruction framework, termed as DNA microscopy43, to perform spatially resolved measurements in cells. DNA microscopy allows RNA profiling at the single-cell level based on the relative positioning of biomolecules that are barcoded by unique molecular identifiers. ST and NGS-based transcriptional profiling methods approach subcellular resolution with genome-scale mapping, providing a convenient tool for cancers.Multiplex visualization of RNA transcripts has been performed at two distinct scales, using either genome-wide or targeted (subset of genome) screens. The first approach has developed sample preparation and barcoding techniques for encoding spatial positions onto the DNA identifier sequence, followed by NGS of positional barcoded transcriptional profiles. In this direction, spatial transcriptomics (ST)49. These methods have provided transcriptional profiles in cultures and brain slices to reveal the spatial organization of cells. SeqFISH+ deserves special attention as the spatial resolution has been reported at the sub-diffraction-limit, highest resolving power in RNA-profiling approaches46. Signal amplification strategies have enabled spatially resolved RNA measurements in breast cancer tissues and larger structures such as zebrafish embryos and thicker brain slices52. Recently, another ligation chemistry was developed for STARmap technology to reconstruct three-dimensional (3D) distributions of transcripts in the mouse brain53. Another technology simultaneously monitored DNA and RNA profiles in tissues using SABER protocols54. To compute significance of spatial distributions, statistical analysis methods are also being developed for both NGS- and FISH-based technologies57. These targeted nucleic acid profiling methods and their computational analyses are on the rise for the analysis of tissues from cancer patients. Although transcriptional profiles work robustly in mouse and fresh human specimens, RNA transcripts in archival FFPE patient samples would be relatively more challenging to measure compared with multiplex protein analysis due to the rapid degradation of RNAs over time.The latter approach has directly profiled a subset of the transcriptome by imaging of hybridized oligonucleotides on transcripts. Multiplexed error-robust fluorescent in situ hybridization (MERFISH) and sequential FISH utilized single-molecule barcoding of individual RNA molecules that were imaged, aligned, and de-barcoded to perform spatially resolved transcriptomics up to 10,000 gene targets58. One such device is atmospheric pressure matrix-assisted laser desorption/ionization (MALDI) that achieved 1.4 µm resolution for imaging metabolites, lipids, and small peptides in single-celled organisms59. Another instrument modification was transmission-mode MALDI (t-MALDI-2) that reduced the imaging pixel size to 0.6 µm when recording lipid profiles in mouse organs such as the brain and kidney60. A separate instrument was also designed as OrbiSIMS to perform high-resolution imaging of cells and tissues for mapping lipids and neurotransmitters in mouse brain tissues. NanoSIMS, a commercially available device, has also been used to profile metabolites, lipids, and sugars for only seven selected channels at 50–100 nm spatial resolution61. Multiplexing metabolites in the same measurement with RNA and protein profiles should impact the current medical practice for cancer diagnosis and treatments.Beyond the central dogma, metabolites also play an important role in the cellular response to therapies. Compared with RNA and protein profiling, it is rather complicated to determine spatially resolved metabolite distributions in native tissues, because metabolites remain noncompatible with amplification methods, the labeling toolkits for metabolites are limited, and the chemical compositions are complex to be deconvolved from mass spectra. Although conventional mass spectrometry imaging (MSI) has provided 5–20 µm resolution for profiling metabolites, recent advancements in instrumentation have allowed acquisition of 1–2 µm spatial features66. Although there is an increasing interest in multiplex RNA and proteins in spatially resolved analysis technologies, only a few transcripts were detected together with multiplex protein maps using mass cytometry and a decent resolution was achieved for simultaneous RNA and protein detection by digital spatial profiling technology67. Spatial RNA mapping is key to decipher short-time response of transcription activity per gene and spatial protein map is crucial to reach functional state of a cell of interest. Technically, RNA-targeting protocols and protein-staining experiments have distinct needs, making it difficult to unify in a simple experiment. First, the blocking steps include serums such as bovine serum albumin, which degrades the quality of RNA molecules during the washes and treatments. Second, the fixation and permeabilization protocols vary to optimize RNA and protein detection protocols. RNAs require gentle permeabilization methods such as cold ethanol to keep transcripts intact, whereas the proteins need harsh treatments such as methanol and triton that might break RNAs into fragments. RNAs are more sensitive to nuclease activity and RNase might degrade RNA quality; on the other hand, proteins are more robust even in RNase-containing buffers. In addition, metabolite imaging in single cells is highly invasive and the samples are ablated during the image acquisition. Therefore, the ideal spatially resolved multi-omics platform would need to perform RNA targeting “first” and protein staining “second”, and metabolite imaging as the “last” step. Technological and biochemical advances would be required to realize such a truly multi-omics platform for spatial analysis of individual cells.Simultaneous imaging of RNA and proteins has been implemented in single-cell analysis for mostly in experiments that utilized disassociation of cells and barcoding with nucleic acids16. On the other hand, the bottom-up model utilizes limited types of cell lines  to form a biomimetic tissue environment by culturing monoclonal cells, two-cell types or three-cell types in controlled environments such as artificial gels, microwells, and prototyped microfluidics interfaces. For both models, solid and liquid biopsies show the spatial heterogeneity in different regions of the same tumor or different locations of distinct tumors in the same patient68. This perspective will focus on the use of spatial multiplex bioimaging to explore the dynamics and complexity of these top-down and bottom-up approaches as a unified framework to decipher disease mechanisms and progressions.To understand the molecular pathogenesis, two complementary directions are presented in this perspective, top-down and bottom-up models. The top-down approach targets intact biopsies that maintain the physiological structures of tumors, providing direct representation of the pathology of the tumor. In this direction, tissues are sectioned from solid tumors and CTCs/CTC clusters from liquid biopsies are isolated, followed by visualization of molecular dynamics at the cellular level with multiplex imaging69. Although many other efforts utilize ensemble level sequencing and cytometry solutions, the presented work will focus on spatially resolved measurements indirectly sectioned and isolated patient samples at the single-cell level.This approach begins with isolating solid or liquid biopsies from patients. Thin tissue specimens are then obtained from tissue blocks that are embedded in preservative chemicals such as paraffin. A microtome is then used to slice the tissue into 5–10 µm sections Fig. . These t71. Quantum dots have been used in immunofluorescence (IF) histochemistry (QDs-IHC) to create five-color IHC maps with a brighter signal73. QD-IHC exhibits more accurate and sensitive results in detecting HER2 in breast cancer than conventional IHC74. QDs can be also conjugated with aptamers to target molecules in a single cancer cell75. A spectral imaging  and Tyramide signal amplification (TSA) scheme have enabled simultaneous eight-color IHC maps in tissue sections from multiple cancers76. The same TSA-based reagent labeling was also automated by Ventana for five-color IHC detection in tissues77. Multiplex IHC technologies have been implemented up to eight markers so far and clinically have been tested. However, multiplex IHC solutions fail to meet the need for uncovering the complexity of a tumorecosystem that contains up to 50 to a 100 distinct cell types and compositions in diseases.Spatial analysis of tissue specimens has classically been performed by immunohistochemistry (IHC) to analyze histopathological features of prognostic factors in distinct tumors. IHC is a colorimetric staining assay based on antibody and color/fluorescence generation chemistry  to measure one biomarker at a time on tissues, followed by bright-field imaging and digital analysis of biomarker expression levels on distinct cell types. In breast cancers, prognostic factors measured by IHC remain as traditional histopathological features for predicting patients’ diseases78. Although IF has been part of ample clinical and research projects, it is still critical to achieving multiplex IF for more data-driven cellular research in immunology and cancer biology. As the genome is composed of about 20,000 protein-coding genes, ideally multiplex IF should get closer to these multiplexing levels for ultimate profiling solution. Furthermore, antibody-based detection of targets suffers from crosstalk among antibodies due to nonspecific binding events. In practice, multiplexing of IF protocols has detected up to 30–50 protein markers in a single experiment. Cyclic labeling and fluorescence imaging methods80  and one-shot MSI of isotope labeling techniques35 (IMC and MIBI) have delivered this premise of measuring up to 56 markers in FFPE tissue sections. For instance, tissue-optimized CycIF profiled a 24-marker panel in patient specimens from pancreatic cancers, providing anti-correlated single-cell distributions of E-cadherin and Vimentin32. These spatially resolved protein-imaging methods are the initial efforts for a spatial proteomics vision81 that impacts next-generation histological analysis.Another conventional method for analysis of tissue sections is IF to study tumor microenvironment. The microscopic examination enables an understanding of molecular information from tissue to single-cell levels. IF allows measurements for more than three biomarkers using antibody sets that are conjugated to distinct spectral dyes50. Spatial maps of prostate cancers were also generated based on ST technology39. Complementarily, spatial metabolomics approaches also provided region-specific small molecule and lipid profiles by mass spectrometry analysis in esophageal tissues58. Instrumental advances82 in MSI methods (MALDI and SIMS) will be applicable to human tissues to distinguish between health and disease form metabolic alterations at sub-micrometer resolution levels.Alternative spatially resolved profiling methods can be applied to the top-down analysis. Spatial genomics and transcriptomics technologies with genome-scale mapping  with the breast cancer cells , and immunotherapies. These patients are mainly classified as “responders” and “non-responders,” which are then used as part of the disease knowledge libraries. This classification can further be converted to decision trees based on the molecular and characteristic information of the individual patients.Multi-omics approaches in personalized medicine have become the recent paradigm in medical practice. Although the advances in genome sequencing have piqued the interest for individualized treatments, other molecular profiling methods to measure the epigenome, transcriptome, proteome, and metabolome, along with environmental factors, have impacted clinical decision-making102. These methods utilize cellular suspensions from patient-derived blood and sorted cells to study the drug resistance in therapies. Single-cell variations uncovered the principles of tumor compositions and specific immune subtypes that differentially change in distinct cancers. For instance, natural killer cells showed the cytolytic activity in lung adenocarcinoma, an emerging cellular phenotype for immunotherapies103. These single-cell methods still lack one more dimension, the spatial extent of cellular interactions104. Spatial-omics technologies that are presented in this perspective are ideal platforms for designing personalized treatments. Single-cell phenotypes and their local interactions are both captured in spatially resolved proteomics, genomics, and metabolomics technologies in patient specimens. Spatial multi-omics approaches will be the ultimate molecular profiles of resistant and response patient groups, together with patient characteristics , pathological maps, and molecular imaging data107. Although these datasets are from the same patients, there is still a challenge of combining these multi-omics data with different characteristics. The performance of patient classifications suffers from these incompatible data types105. To address these issues, spatial multi-omics approaches offer high sensitivity and direct visualization of transcript, protein, and metabolite profiles that will potentially improve the patient stratification. Spatial profiles are well-aligned at the cellular level, making them consistent across individual patients or across patient cohorts. Another significant challenge for machine learning in medicine is the size of the datasets. Although genome-level sequencing results are valuable, spatial single-cell data generates massive molecular maps at the subcellular and single-cell levels. This cellular big data generated by the spatially resolved analyses will then enhance the performance of automated stratification of patient groups. Generated from spatial multi-omics platforms, smart patient classifications can then be integrated with drug response to a given therapy. This comprehensive information about patient groups (responder vs. non-responders) and correlation to molecular subtypes of patients  will serve as “training” of the machine- and deep-learning algorithms, initial step of the precision oncology framework  was used to incorporate cellular developmental aberrations to predict the relapse of leukemias113. Similarly, a subset of glioblastoma cells were identified as the response predicter from single-cell signaling profiles without the need for prior information about the expected cell clusters on bioinformatics maps114. When combined with spatial multi-omics technologies, bioimaging features  from single-cell analysis and molecular imaging will continue impacting predictive outcome studies in precision medicine.Using artificial intelligence solutions, prediction of patients’ treatment outcome has been applied to imaging data and molecular maps in particularly radiology and pathology115. The 3D dose–response in 120 patients with head and neck cancers was predicted by a deeply connected U-net method, providing accurate 3D distributions of the radiotherapies in the head and neck regions116. In addition to personalized dose design, artificial intelligence aids the development of combinatorial drug treatments. Tumor cell lines were studied in response to a high-throughput drug screening assay, wherein a deep neural network model showed higher efficacy for more than two-drug combinations. Synergistic combinations of drugs were also explored by a DeepSynergy algorithm that utilized machine learning on experimental and clinically approved drugs on cell cultures117. The presented framework in this paper that combine top-down and bottom-up approaches can benefit from personalized dose predictions on multiplex histological analysis from patient biopsies, and at the same time, combinatorial drug treatments can be optimized by spatial-omics maps on high-throughput cell cultures to reveal synergistic drug groups in cancer treatments.Another benefit of the machine-learning algorithm is to design a personalized dose for therapy. A deep profiler framework was developed using pretreatment CT scan images that identified radiotherapy dose requirements for individual patientsThe prediction model in spatial multi-omics data can greatly enhance the accuracy and sensitivity compared with the classical omics data as the dynamic range of cellular phenotypes and their corresponding functional differences can be mapped out with finer details. The rare and dominant cellular subgroups can be classified into two distinct clusters in the prediction to balance the dynamic range differences of heterogeneous cellular populations. In other words, the average spatial multi-omics data already contains information that would be captured by conventional sequencing approaches, but now also with additional sub-composition information as an additive parameter in the model. The larger and the more intricate the input data is, the finer the predictions will be for precision oncology. For instance, in a supervised prediction model, the spatial extent of biopsies can be calculated to define immune- and tumor-enriched regions. These larger-sized tissue architectures can then be separated into cellular distributions for a given area. For each cell, subcellular variations can be computed to further regularize predictions. Of note, these hierarchical structures are connected and reach the full extent of biopsy samples at different spatial details. Briefly, the tissue neighbors would show data that is split into, e.g., 10 × 10 regions. The single-cell distributions would split into 100 × 100 spatial features and subcellular differences would further classify the same tissue to 1000 × 1000 data points. A supervised model can then use this well-defined data structure to output consistent predictions for personalized therapies.118. However, it necessitates stringent processing conditions for robustness and performance comparisons with classical machine-learning algorithms such as support vector machines and random forest trees for validation119. Comparative analysis of complex models120 from conventional multi-omics data and computational predictions121 from emerging spatial multi-omics data would be one of the central debates for next-generation precision oncology contexts.The projected use of artificial intelligence algorithms for modeling spatial multi-omics data is feasible based on spatial imaging features such as 3D distributions, lateral and axial dimensions, orientation in 3D space, and encoded colors122. Such a reference map handles high-dimensional mass cytometry data from single-cell details. The relative positioning of immune-cancer cells from fine pathological data can be incorporated into landmarks, creating a spatial reference framework per patient. The cellular neighborhood approach also was introduced in the CODEX proteomic analysis to define normal and diseased spleen architecture from multiparameter single-cell maps33. Spatial multi-omics datasets can use a hybrid model to compute hierarchical neighborhoods and a reference framework to capture patient-to-patient variations. Graphical representations and network biology would be the single-cell route to visualize and handle this spatial complexity without overfitting issues.Finally, spatial features from tissue compositions, cellular positions, and their relative distributions, and subcellular heterogeneity would need a “reference framework” to construct relative spatial maps for each individual patient. Previously, a dynamic immune system was modeled by a “scaffold” framework to define reproducible cell populations in mouse and human immune systems across various organsTumors are complex ecosystems that are composed of multiple cell types interacting with each other. Cancer cells respond to therapies under the effect of other cell types in the tumor microenvironment. To decipher heterogeneity of drug treatments in distinct patients, cellular compositions need comprehensive molecular characterization at the single-cell level. During the metastasis, these cellular types leak into the bloodstream until they reach a secondary location. Through liquid biopsies, analysis of clustered cells and isolated cells in the bloodstream also provides complementary information about the patient’s phenotype and potential for therapeutic design. To this end, spatially resolved bioimaging technologies reveal genomic, proteomic, and metabolic variability of individual cells in both solid and liquid biopsy samples. This approach provides a top-down approach to study disease progression.To recapitulate the heterogeneity of tumor microenvironment, an increasing number of approaches have been developed to mimic patient samples in artificial on-chip environments. Tissue-engineered platforms and organs-on-chip are designed to study cellular interactions with a controlled in vitro model to understand one-cell, two-cell, or multi-cell groups for monitoring dynamics and combinatorial drug treatments. Multiplex bioimaging methods can elucidate upstream and downstream factors in these bottom-up bioengineering platforms, providing complementary information about the molecular profiles and response characteristics of patient-derived cells.Multiple biomarkers in the tumor, cell cluster, and subcellular microenvironment will be measured in a group of patients who respond or resist therapies. An artificial intelligence method such as a machine or deep learning will then use these spatial multi-omics maps with significant features as training datasets. Learning-based predictions will then make it possible to group and personalize the new patient’s treatment according to the spatial-omics information that associates the physical, genetic, and biochemical properties of the tumors. The presented AI-based high-dimensional histological analysis pipeline will be validated in clinical trials with expected outcomes and matching predictive scores, demonstrating the use of a precision medicine approach and beyond.


The desperate need to find drugs for COVID-19 has indicated repurposing strategies as our quickest way to obtain efficacious medicines. One of the options under investigation is the old antimalarial drug, chloroquine, and its analog, hydroxychloroquine. Developed as synthetic succedanea of cinchona alkaloids, these chiral antimalarials are currently in use as the racemate. Besides the ethical concern related to accelerated large-scale clinical trials of drugs with unproven efficacy, the known potential detrimental cardiac effects of these drugs should also be considered. In principle, the safety profile might be ameliorated by using chloroquine/hydroxychloroquine single enantiomers in place of the racemate. The current 2019-nCoV (SARS-CoV-2) causing COVID-19 has demonstrated that our preparedness for emerging respiratory viral infections is far too inadequate. In the absence of any known efficacious therapeutic agent or vaccine, our effective measures against the disease are somehow similar to our ancestors a century ago: constant hand washing and social distancing. The desperate need to find drugs for COVID-19 has brought the attention of numerous scholars to the repurposing of known drugs as tentative COVID-19 therapeutics.It is logical that repurposing known drugs for COVID-19 may be first considered from pharmacological agents with proven efficacy against other highly pathogenic viral infections. However, useful medicines could also be identified from non-etiotropic drugs. In the hunt for new antiviral agents, repurposing old drugs or repositioning experimental ones are not novel approaches, and they were proposed early to treat other viral infections such as the Ebola outbreak , while eAccordingly, antiviral agents, cardiovascular drugs , and traOne of the options under investigation is the old antimalarial drug, chloroquine—a synthetic succedaneum of cinchona alkaloids  Figure —which gaBesides the ethical concern related to accelerated large-scale clinical trials of drugs with unproven efficacy ,12, the However, it should be noted that chloroquine is a chiral compound currently used as a racemate. It is generally accepted that stereochemistry may affect both the pharmacodynamics and ADMET of drugs . Stereoc50 values ranging from 2.5 to 19.7 μM, depending on the experimental setting used [Chloroquine is a moderate inhibitor of hERG with electrophysiology studies indicating ICing used . Site-diing used . A thirding used . Thus, aAs stated above, chloroquine may prolong the QT interval of the electrocardiogram (ECG) and cause the potentially lethal long QT (LQT) syndrome (LQTS), also interfering with the normal activity of Kir2.1 potassium channels with the binding site being located in the cytoplasmic pore of the channel . SelectiS)-enantiomer displaying higher affinity than its enantiomer [50) is lower for the (S)-enantiomer in the mouse model [ADMET aspects might also contribute to the stereoselective clinically relevant outcomes. The binding of chloroquine with human plasma protein is enantioselective with the (antiomer ; furtherse model .On the contrary, the desirable activity on COVID-19 would mostly stem from structurally unspecific aspects such as basicity and lipophilicity. The formation of SARS-CoV particles occurs in the Golgi apparatus . ChloroqBoth enantiomers of chloroquine may be easily obtained from the corresponding racemate by optical resolution  or throuOn the other hand, the studies on chloroquine enantiomers as antivirals might boost endeavours aimed at developing new chloroquine chiral analogs starting from this lead compound. Medicinal chemists have developed easy rules of thumb to both accelerate optimization  and reduGiven that useful drugs for treating new diseases might stem from relatively old drugs and that chloroquine has already shown some promise in pilot COVID-19 trial studies, its full potential should be explored by testing enantiomerically pure analogs. Beyond establishing the relative potency of the enantiomers against SARS-CoV-2, their differential cardiotoxicity could lead to the identification of a more potent and safer lead.


The social determinants of health are a decisive yet persistently understudied area for tackling global health challenges like antimicrobial resistance (AMR). Precarity is one determinant whose importance is increasingly recognised, which we define here as ‘a form of pernicious self-dependence that undermines individuals’ control over their own lives and limits their ability to flexibly respond to crises’. We aimed to assess the relationship between precarity, other forms of deprivation and healthcare-seeking behaviour by asking, ‘What is the impact of precarity, marginalisation and clinical presentation on healthcare-seeking behaviour?’ and ‘Do patients experiencing precarious livelihoods have clinically less advisable healthcare-seeking behaviour?’We used healthcare-seeking behaviour census survey data from rural Thailand and Laos, wherein five rural communities were surveyed two times over a period of 3 months . Using descriptive statistical and multivariate logistic regression analysis on the illness level, we studied precarity alongside clinical presentation, marginalisation and facilitating solutions during an illness  as determinants of healthcare-seeking behaviour in the form of healthcare access and antibiotic use.The data included 1421 illness episodes from 2066 villagers. Patients in precarious circumstances were up to 44.9 percentage points more likely to misuse antibiotics in the presence of situational facilitators (predicted antibiotic misuse: 6.2% (95% CI: 0.9% to 11.4%) vs 51.1% (95% CI: 16.6% to 85.5%) for precarious circumstances with/without facilitation). Marginalisation was linked to lower antibiotic use, but this did not translate into clinically more advisable behaviour. Clinical presentation played only a minor role in determining healthcare access and antibiotic use.This study underlines the importance of context and local livelihoods in tackling drug resistance. While supporting the growing emphasis on AMR-sensitive development policy, we call for future research to study systematically the healthcare-seeking behaviour impact of precarious livelihoods, social policy and community development initiatives.NCT03241316. Precarity is an important social determinant of health with demonstrated physical and mental health consequences, which affects people in low-income, middle-income and high-income contexts.The response to antimicrobial resistance (AMR) in human use focuses heavily on individual behaviour change, neglecting the critical role that contextual determinants play in behaviours of individuals.The behavioural impact of precarious livelihoods is hypothesised to contribute to clinically inadvisable antibiotic use and therefore potentially to AMR.We used an original micro-level behavioural data set from Thailand and Lao People’s Democratic Republic, which enabled us to disentangle clinical presentation, marginalisation and precarity as separate drivers of healthcare-seeking behaviour.The link between clinical presentation and antibiotic use was surprisingly weak. Instead, patients in precarious circumstances were significantly more likely to misuse antibiotics in the presence of situational facilitators .This is the first study that examined quantitatively the relationship between precarity and AMR-related healthcare-seeking behaviour.Development processes that change whether and how people experience precarious circumstances could have unforeseen implications for collective global health threats such as AMR.Our study lends support to ‘AMR-sensitive development policy’: global health interventions must move beyond patient-centric and disease-centric approaches, acknowledging and responding to contextual factors that shape how people cope with illness.If precarity as a social determinant continues to be neglected, then localised forms of hardship could unwittingly influence and undermine the effectiveness of existing clinical and behavioural interventions to tackle AMR.Global health research and practice have become increasingly sensitive to non-clinical factors that influence health outcomes. This recognition has been driven by the concepts of the ‘social determinants of health’ and ‘diseases of poverty’,One key domain of demand-side responses to AMR is population behaviour change towards a more targeted use of antimicrobials such as antibiotics .In a growing and interdisciplinary body of literature, precarity has been receiving attention as a social determinant of health,21 22Patients in such circumstances could find themselves pressured into detrimental patterns of self-medication and delayed healthcare access. Consequently, ‘quick fix’ solutions such as self-medication or antibiotic purchase without prescriptions can be interpreted as default strategies to cope with adversity,We situated our research in rural Thailand and Lao People’s Democratic Republic (PDR), considering that Southeast Asia is an epicentre for antibiotic resistance.45Compared with Lao PDR, Thailand exhibited a more advanced economic and health system context, evidenced by virtually zero extreme monetary poverty at US$1.90/day (Lao PDR: 23%), government healthcare expenditure of 15% of total government expenditure (Lao PDR: 4%) and an expected life expectancy at birth of 75 years (Lao PDR: 67 years) , which were selected in consultation with local stakeholders. Guiding selection criteria were (1) village size and structure, (2) remoteness, (3) village-level infrastructure, (4) ethnic composition and (5) available health facilities. The villages had an estimated population between 339 and 1462 residents , representing 3331 villagers in total .This project was based on preceding qualitative research with local northern Thai patients and the general public,We implemented two-round individual-level census surveys in the five purposively selected villages between November 2017 and May 2018. Within the selected communities, all households were enumerated using a satellite-aided sampling approach,We chose an oral consent procedure to not alienate and discriminate against illiterate or unregistered participants. Of the 3744 responses across the two survey rounds, we did not receive consent for data sharing in four instances, which constitutes the main difference between the data analysed here and the publicly available data set of our project.Based on the complete village census, we identified as patients those respondents who had an acute illness and injury in the 2 months preceding the survey (or children below 18 years who experienced an illness episode under the respondents’ supervision).Our survey instrument was a 45-min face-to-face questionnaire see , adminis10.1136/bmjgh-2020-003779.supp2Supplementary dataThe questionnaire modules included (1) basic demographic and socioeconomic information, (2) social network structures, (3) antibiotic-related knowledge and attitudes and (4) healthcare-seeking behaviour during acute illnesses and accident-related injuries. This study used in particular modules 1 and 4, whereby 60.8% (1256/2066) of the participants volunteered information about at least one recent illness episode, experienced by themselves or a child under their supervision within the 2 months preceding the survey, yielding a total of 1887 illness episodes captured by the community census surveys (1421 episodes after removing ongoing/yet unresolved illnesses).The healthcare-seeking data included self-reported symptoms and recalled diagnosis of the condition as well as detailed information about each step of the illness episode, from the moment a discomfort was detected until the condition was resolved or the symptoms otherwise disappeared. For every step, the type of treatment was recorded  together with the type of received medication  and supplementary information about the process . This information formed the basis for our analysis of healthcare-seeking behaviour: on the one hand, these data documented ‘revealed’ healthcare-seeking behaviour during the course of an illness episode . On the other hand, the rich information on self-described symptoms and recalled diagnoses  permitted a broad categorisation of clinical presentation see  and—toge10.1136/bmjgh-2020-003779.supp1Supplementary dataConsidering the importance of antibiotics for our research question, we focused on cases where we could establish antibiotic use with a high degree of confidence. We manually coded free-text responses of reported medicines and treatments based on pharmaceuticals in local circulation, and triangulated ambiguous responses  based on respondents’ reports as to how they colloquially refer to antibiotics in module 3 of the survey questionnaire. Of all 4611 recorded medicine use episodes, 13.5% (623/4611) involved an antibiotic with a high degree of confidence .The key independent variables comprise clinical determinants and composite indexes of precarity, marginalisation and situational facilitation. We explain these variables below and provide variable summaries in The impact of each clinical determinants, their duration, severity  and frequency over the past 6 months was evaluated on healthcare-seeking behaviour. The nine clinical determinants were based on respondents’ reported symptoms and recalled diagnosis during medical encounters, and included presence of an infection, fever, respiratory tract symptoms, common cold (defined as the presence of runny nose and/or sneezing and/or cough), digestive symptoms, uro-gynaecological symptoms, neurological symptoms, traumatism and other symptoms. For full disclosure, the complete list of symptoms and their coding into clinical presentation is provided in The health impact of precarity is often framed in similar terms as problematic employment conditions, poverty/marginalisation (which are often used synonymously) or stress.21 25 63In order to represent the distinct circumstances under which precarity could militate against healthcare-seeking behaviour, we considered six indicators along occupational, social and logistical dimensions. The occupational dimension clearly cannot be ignored in a study of precarity and evaluated whether (1) people’s income was erratic  and whether (2) the structure of the work restricted people’s flexibility when responding to illnesses .Marginalisation was evaluated in terms of education, wealth and ethnicity21 67In addition to the indicators above, situations and practical means may arise situationally—that is, revealed during the course of an illness—which enable patients to respond to crisis and escape the structural conditions of precarity and marginalisation.Outcome variables considered in this study comprised different forms of access to healthcare and antibiotic use to represent healthcare-seeking behaviour. Reflecting the pluralistic health systems of Southeast Asia,We complemented the analysis of healthcare-seeking behaviour with an evaluation of clinically ‘inadvisable’ antibiotic use and healthcare access. These evaluations used the comprehensive survey data surrounding patient clinical presentation , which enabled us to identify cases in which healthcare access and antibiotic use appeared clinically advisable but was not pursued (or vice versa). Given the nature of the self-reported data, we did not flag behaviours as ‘inadvisable’ where the clinical indication was unclear. The evaluation was carried out by a medically qualified co-author with research experience in the field sites (TA) in correspondence with the local research teams.Specifically, the first clinical indicator (outcome variable ‘Inadvisable_Access’) evaluated the rationale for seeking no formal treatment despite a clear indication for care, and vice versa. The second clinical indicator evaluated the rationale for antibiotic use (outcome variable ‘Inadvisable_Antibiotic’), whereby our focus was on identifying situations with a clear indication to not use an antibiotic, namely scrub typhus, leptospirosis, cholera and malaria. We also flagged as inadvisable where patients used antibiotics despite the absence of a clear indication for an antibiotic  and when patients accessed antibiotics stored at home, from friends, traditional healers and other unregulated sources of healthcare Note that these evaluations are strictly from a clinical perspective and do not entail judging villagers’ behaviour as ‘unreasonable’—our argument rather is that people have good reasons and structural determinants for behaving the way they do, and that this behaviour might look clinically inappropriate but requires non-clinical responses.The analysis took place on the illness level, whereby we analysed the pooled sample of all completed illness episodes across the two survey rounds. We proceeded in four steps:Step 1: descriptive analysis of the healthcare-seeking behaviour, precarity and marginalisation among the study population.Step 2: contribution of the clinical determinants on healthcare-seeking behaviour.Step 3: contribution of precarity and marginalisation on healthcare-seeking behaviour.Step 4: the role of precarity as contributor to clinically inadvisable healthcare access and antibiotic use.2 tests for binary and Wilcoxon rank-sum tests (two-sided) for non-normally distributed variables to test for differences across sites.70 71Step 1 was based on descriptive statistical analysis to describe and contrast the survey results across the two field sites. We compared the samples on the illness level, using χWe conducted univariate and multivariate regression analyses for Steps 2–4 see . Step 3 For all analyses in Steps 2–4, we first conducted univariate analyses, followed by multivariate analyses adjusted by confounders (the main results only contain multivariate results for simplicity). We initially chose multilevel logistic regression models with a site random effect.We also conducted the multivariate analysis with an interaction effect between precarity index and facilitation index to consider the potential moderating effect of the situational facilitators. We compared model fitness between the interacting and non-interacting models based on the significance levels of the parameter estimate for the interaction term and the Akaike Information Criterion AIC, reportiOur robustness checks (not reported here) included comparisons between the single-level and multilevel models and estimations using nested models and including patients’ symptoms as additional determinants of health behaviour. The robustness checks did not substantively influence the main results nor the conclusions of this paper.We used the statistical software Stata V.15 (StataCorp). Throughout the results, we indicated significance levels below 0.1, 0.05 and 0.01 with *, ** and ***, respectively, and considered the 5% level as ‘statistically significant’.Across the 1421 illness episodes in the sample , the most commonly reported illness was the presence of any infection with 64.6% (918/1421) of all cases, while the term ‘fever’ was cited in 25.7% (365/1421) of all cases. Respiratory tract symptoms were the second most reported complaint , of which common cold represented 56% (519/925 cases). Median duration of symptoms was 5 days (IQR 3–8), with a moderate severity. 79.2% (1125/1421) of illnesses involved access to healthcare and antibiotic intake was declared in 28.7% of all cases (408/1421).The results of the descriptive statistical comparison of the two field sites are presented in In the multivariate analysis of clinical determinants, the common cold was the only clinical presentation significantly associated with healthcare access (83.2% (432/519) of all common cold episodes; adjusted OR (aOR) 1.63 (95% CI: 1.17 to 2.26)). However, antibiotic intake for common cold was not significantly greater than other clinical presentations , whereas the presence of an infection was significantly associated with antibiotic intake , as was respiratory tract infection . The presence of digestive symptoms was significantly associated with antibiotic withholding . The association between clinical determinants and outcomes is illustrated in The interaction terms were not statistically significant for healthcare access and antibiotic use. Instead, facilitation presented a strong and statistically significant positive association with healthcare access  and antibiotic use  independently of precarity. The marginalisation index was associated with antibiotic withholding at the 1% level , whereas the precarity index was not statistically significantly associated with healthcare access or antibiotic use. Among other control variables, illnesses involving children, those with a higher severity rating and illnesses with a duration between 7 and 30 days were significantly more likely to involve healthcare access and antibiotic use.This final step assessed the factors contributing to clinically advisable healthcare-seeking behaviour . The intOur research focused on the clinical and socioeconomic determinants of healthcare-seeking behaviour. We drew on a uniquely detailed health behaviour census survey from five villages across northern Thailand (Chiang Rai province) and southern Lao PDR  as part of a broader healthcare-seeking behaviour research project.Patients commonly exhibited respiratory tract symptoms, presence of an infection and fever. Precarious livelihoods were common in both sites, whereas indication of marginalisation was less widespread and more concentrated in Chiang Rai.The common cold was positively associated with healthcare access whereas presence of an infection and respiratory tract symptoms were positively associated with antibiotic use. The surprisingly minor link between clinical presentation and antibiotic use pointed towards the importance of other, social determinants of healthcare-seeking behaviour, such as precarity and marginalisation.We analysed the contribution of marginalisation and precarity to healthcare-seeking behaviour and found that situational facilitators were associated positively with healthcare access and antibiotic use, but no moderating effect of facilitation on precarity materialised. Marginalisation was negatively associated with antibiotic use. However, overall rates of healthcare access and antibiotic use could mask clinical justifications for the observed behaviour.An evaluation of clinically inadvisable healthcare access and antibiotic use showed that facilitation independently linked to more appropriate healthcare access. In addition, controlling for other determinants of health behaviour, patients in otherwise precarious circumstances were significantly more likely to misuse antibiotics in the presence of situational facilitators.To the best of our knowledge, this is the first study that examined quantitatively the relationship between precarity and AMR-related healthcare-seeking behaviour. A key strength of our study is the original micro-level behavioural data set, which enabled us to disentangle clinical presentation, marginalisation and precarity as separate drivers of healthcare-seeking behaviour.Key limitations in this study pertain to the nature of the study sample, which reflects living conditions in selected rural communities of Chiang Rai and Lao PDR rather than broader rural populations of low-income and middle-income countries. More extensive and systematic health behaviour surveys based on our in-depth survey instrument would be necessary to understand the broader variability of common healthcare-seeking behaviours and their socioeconomic drivers. In addition, observational survey data on healthcare-seeking behaviour are subject to recall biases that could amplify the perspectives of relatively more privileged groups.The increasing health policy interest in the 2030 Sustainable Development AgendaOur study has two main implications. On the one hand, global health interventions must move beyond patient-centric and disease-centric approaches, acknowledging and responding to contextual factors that shape how people cope with illness. Interventions based on this logic could for instance aim at improving work environments, supporting social support structures and fostering community building. On the other hand, our study emphasises that localised forms of hardship could influence the effectiveness of clinical interventions to tackle AMR—an issue that is often overlooked in global health practice.


Macaca thibetana) provide an example of male sexual interference that can be used to discuss the sexual competition hypothesis. We found male sexual interference in this species showed significant seasonal variation. Age did not affect the proportion or type of interference behaviors that a male performed, but his social status did. Dominant males more often interrupted copulations. Subordinate males more often directed harassment behaviors toward dominant males, which reduced copulation duration, especially the post-ejaculatory phase of copulation. Our results suggest that sexual interference (interruption or harassment) may be a tactic to reduce the mating success of other males by either preventing ejaculation or reducing the duration of the post-ejaculatory phase, which is critical for sperm transport and, thus, reproductive success. Sexual interference behaviors (interruption/harassment) by male nonhuman primates can lead copulating individuals to separate and is hypothetically a form of male–male competition for access to sexually receptive females. Tibetan macaques (Macaca thibetana) in mating and non-mating seasons at Mt. Huangshan, China, from August 2016 to May 2017. Our results showed that males’ individual proportion of copulation interruption and harassment was higher during the mating season than during the non-mating season. In addition, dominant males more often performed interruption, whereas subordinate males more often performed harassment. We found no difference in the individual proportion of copulation interruption or harassment between adult and subadult males. Adult and subadult males both directed copulation interruption and harassment more often toward the mating male than toward the mating female. Lastly, the post-ejaculation phase of copulation was shorter when copulation harassment occurred than when it did not. Our results suggest that sexual interference may be an important mating tactic that adult and subadult males use in male–male sexual competition. Male nonhuman primate sexual interference, which includes copulation interruption and copulation harassment, has been related to reproductive success, but its significance has been challenging to test. Copulation interruption results in the termination of a copulation before ejaculation, whereas copulation harassment does not. We conducted this study using the all-occurrence behavior sampling method on sexual interference behaviors of seven adult and four subadult male Tibetan macaques ( A male’s reproductive success may change throughout the male’s lifetime, and the use of an effective mating strategy may assist the male in male–male mating competition . A male Macaca arctoides), chimpanzees (Pan troglodytes), golden snub-nosed monkeys (Rhinopithecus roxellana), and squirrel monkeys (Saimiri collinsi) .,49.48,49The same adult and subadult males  were present in the group during both mating and non-mating seasons. We first calculated a total proportion of sexual interference in each seasonal study period as the number of sexual interference events divided by the number of copulatory events. For example, if we recorded 15 copulatory events and 10 sexual interference events during the mating season, the total sexual interference proportion during the mating season was 10/15. We used the same method to calculate the total proportion of interruption and harassment behaviors in each season.Additionally, we calculated the proportion of sexual interference events each individual was involved in  as the number of sexual interference events performed by each individual divided by total copulations in which interference took place in each season. Similarly, we calculated the individual sexual interruption proportion and individual sexual harassment proportion.p > 0.05). The data were not normally distributed for each male for season, age, rank, and interference target (p < 0.05), so we used non-parametric statistics for these results. We used a Wilcoxon signed-rank test to compare the total proportion of interruption or harassment events during the mating and non-mating seasons. We used Spearman’s rank correlation to test the correlation between the individual proportion of sexual interference  and David’s scores in mating season. We used a Mann–Whitney U test to test for differences in the individual proportion of interruption or harassment events by adult and subadult male group members in mating season. Based on the direction of interruption or harassment, we used a Wilcoxon signed-rank test to compare the medians of sexual interference based on males’ age class (adult or subadult) and based on whether they targeted the mating male or the mating female in their interference behaviors in mating season.We used descriptive statistics to quantify the following variables: total proportion of copulations with sexual interference in mating season and non-mating seasons; total proportion of occurrences of harassment and interruption in mating and non-mating seasons; and the total proportion of copulations with ejaculation. We used a one-sample Kolmogorov–Smirnov test to examine whether the individual proportions of interference, interruption, and harassment data conformed to a normal distribution (p > 0.05). Since the data were normally distributed in mating season, we used a paired-sample t-test to compare average total copulation durations by season and pre- and post-ejaculatory total copulation durations with and without interruption or harassment in the mating season. All statistical tests were two-tailed with α set to 0.05 [We used a one-sample Kolmogorov–Smirnov test to examine whether copulation duration conformed to a normal distribution  occurred during the non-mating season, and 654 (96.46%) occurred during the mating season. Of the total copulations, the proportion of copulations with observed ejaculation was 85.78% (561/654) compared to 14.22% (93/654) without observed ejaculation in the mating season.We observed sexual interference in 51.92% (352/678) of copulations during mating and non-mating seasons. Harassment constituted 85.80% (302/352) of the sexual interference events, and interruption constituted 14.2% of interference events (50/352). We observed sexual interference during 53% (349/654) of copulations in the mating season and 12.5% (3/24) of copulations in the non-mating season.Z= −2.818, p = 0.005 < 0.050, The individual proportion of interruptions by males was higher during the mating season  compared to the non-mating season  compared to the non-mating season  and subadult males in the mating season  and subadult males in the mating season  than toward mating females in the mating season  more often than toward mating females in the mating season  was shorter than the duration of copulations without interference in the mating season  was shorter than the duration of copulations without harassment in the mating season  or without harassment  [Male sexual interference varies seasonally in nonhuman primates ,55. Duriigritus) ,58. For Although Tibetan macaques may copulate throughout the year, births occur mainly during the non-mating season ,34. ThusSexual interference by adult males ends in one of two ways, depending upon the relative dominance ranks of the individual performing interference and the mating individuals . In the It has been suggested that one of the benefits of copulation interruption by dominant males is decreased mating success of subordinate males through prevention of or reduction in competitors’ mating success before ejaculation . HoweverM. fuscata) [In contrast, although harassment by subordinate males rarely prevented ejaculation or interrupted copulation, it nonetheless could reduce reproductive success by reducing the post-ejaculatory period ,65. Somefuscata) . Bruce (fuscata) ,16. HaraPan paniscus) and Cebus apella [Harassment direction by males may be a form of sexual competition in the mating season, during which the mating male was more often the target of aggression from harassers than was the mating female ,67,68. Os apella ,69. ThisHowever, aggression during mating interference followed a pattern similar to overall male–male aggression . An alteIn macaques and chimpanzees, mating aggression by the male engaged in interference is sometimes directed toward the mating female so that risks may be incurred by the mating male or the mating female. Punishment of the mating male and mating female may also occur sometime after the observed copulation ,70,71,72Niemeyer and Chamove (1983) presented quantitative data demonstrating that interruption before ejaculation successfully prevented ejaculation. They also stated, but provided no quantitative data, that harassment shortened the post-ejaculatory phase of mating males. The data we collected provide empirical support for their observation. If sperm transport is facilitated in part by the length of the post-ejaculatory phase and, thus, ultimately affects reproductive success , then haIn contrast to their rare participation in interruption, lower-ranking males in our study population were active in harassment during the post-ejaculatory phase , which mM. nemestrina) may interfere with one another’s sperm in much the same way [Transcervical sperm transport in rats required 6–10 min after ejaculation or completion, and even a single intromission 2 min after ejaculation could dislodge the vaginal plug and disrupt sperm transport ,74. Brucsame way . A shortIn conclusion, our results suggest that sexual interference in a hierarchical, multi-male/multi-female primate social group may be a male intra-sexual competition strategy. We could not provide direct evidence that sexual interference reduced the likelihood of conception of the mating males and increased mating opportunity and success of the harasser, but our results show that copulation harassment shortens the duration of the post-ejaculation phase of copulations, which likely leads to lowered rates of conception. Our study is foundational in showing the potential importance of sexual interference for intra-sexual competition in Tibetan macaques. Future studies can build from our findings to provide data predictive of conception success in both the harasser and harassed individuals.


Human cells are equipped with a plethora of antiviral proteins protecting them against invading viral pathogens. In contrast to apoptotic or pyroptotic cell death, which serves as ultima ratio to combat viral infections, these cell-intrinsic restriction factors may prevent or at least slow down viral spread while allowing the host cell to survive. Nevertheless, their antiviral activity may also have detrimental effects on the host. While the molecular mechanisms underlying the antiviral activity of restriction factors are frequently well investigated, potential undesired effects of their antiviral functions on the host cell are hardly explored. With a focus on antiretroviral proteins, we summarize in this review how individual restriction factors may exert adverse effects as trade-off for efficient defense against attacking pathogens. Restriction factors are structurally and functionally highly diverse cellular proteins that represent important effectors of the early immune response and may target viral pathogens by numerous mechanisms at essentially every step of their replication cycle . The terDue to their rapid replication rates, enormous number of progeny, and frequently high mutation rates, many viruses quickly adapt to their respective host environments. Altogether, viruses have evolved sophisticated strategies to evade or directly counteract many restriction factors. For example, they frequently mimic the properties of their host cells to avoid recognition by the cell. In addition, viral pathogens may capture cellular genes and transform them into effective tools against antiviral defense mechanisms . This noOne formidable challenge for the host is the evolution of antiviral factors that effectively protect against foreign viral invaders without harming the cell. While it is advantageous for adaptive immune mechanisms to be specific for individual invading pathogens, innate immunity must provide broad-based protection against a huge variety of diverse potential viral invaders. This includes viruses that the individual or even the entire host species has never encountered before. Thus, it is obvious that innate immune factors need to strike a fine balance between protection against a broad range of viral pathogens and limiting the risk of unwanted off-target effects on the host organism. Effective antiviral defense mechanisms might cause undesired adverse effects by numerous mechanisms, for example because the antiviral factors do not perfectly distinguish between self and foreign, or because virus-dependency factors that are depleted are also important for cellular functions. In addition, immune activation alters the concentrations and activities of several cellular factors, many of which also fulfill important physiological functions. Finally, multiple cellular resources and machineries are redirected for defense or shutdown, so that they cannot perform their regular functions anymore. Altogether, it is evident that there are trade-offs between effective innate antiviral immune mechanisms and potential side effects on the host cell and consequently organism.The molecular mechanisms of antiviral restriction factors and their viral antagonists have received substantial attention and have been the topic of several in-depth reviews . In contIn order to replicate, viral pathogens must deliver their genetic material into the host cell. Preventing entry of enveloped viruses is advantageous for the host because it minimizes potentially harmful interactions with the pathogen and avoids manipulation of the host cell by intracellular viral factors. Individual cells may prevent entry of enveloped viruses by selfish or selfless mechanisms: Cells may exclusively protect themselves by downmodulating cellular receptors and cofactors required for infection or by expressing antiviral factors that inhibit fusion with viral particles. Alternatively, infected cells may prevent incorporation of functional viral envelope proteins in progeny virions or induce the incorporation of cellular factors that reduce viral infectiousness and, thus, protect bystander cells rather than themselves. All these modes of action are non-exclusive, and, as outlined below, some antiviral factors may act in both the viral target and producer cells.Several cell-intrinsic entry inhibitors exert very broad antiviral activity. For example, members of the IFN-induced transmembrane (IFITM) family have been reported to protect cells against a large variety of viral pathogens  , left. AAnother antiviral host protein modulating membranes is the IFN-inducible cholesterol-25-hydroxylase (CH25H). This factor inhibits not only fusion during entry of a variety of enveloped viruses   but alsoWhile IFITM proteins and CH25H seem to mainly (but not exclusively) exert their effects in viral target cells , the ant−/− mice show no obvious phenotypic defects  suppresses reverse transcription of various retroviruses by creating a cellular environment that is not permissive for viral replication . SpecifiBefore retroviral DNA can be integrated into host chromosomes, subviral complexes need to enter the nucleus via nuclear pore complexes. This step is inhibited by the IFN-inducible protein MxB , which dIntegration of the linear retroviral dsDNA into the host genome is essential for efficient viral transcription and productive infection. This step is inhibited by KRAB-associated protein-1 (KAP1), also known as TRIM28, another member of the TRIM family . KAP1 inViral pathogens must exploit cellular machineries for efficient transcription of their own genes, and recent studies suggest that some IFN-inducible antiviral factors limit the availability of cellular transcription factors to inhibit viral pathogens. Initially, it has been reported that TRIM22 suppresses basal HIV-1 transcription as it inhibits binding of the transcription factor Sp1 to the HIV-1 LTR promoter via a poorly described mechanism , left. Menv gene rather than overall CpG frequency determines ZAP sensitivity of primary HIV-1 strains  may distinguish cellular from viral mRNAs. These characteristics are exploited by antiviral host factors such as ZAP, SLFN11, PKR, or IFITs to preferentially target viral transcripts . However strains . The CpG strains . Most imd region , right.OAS3 has recently also been shown to rescue replication of viruses with elevated CpG dinucleotide numbers, similar to a knockout of ZAP  that are induced by IFN and activated by dsRNA , left. Tt of ZAP . Thus, bt of ZAP , but subt of ZAP . NotablyTRAILR4 transcripts  . IFIT1 pIn comparison to IFITs, another innate immune factor, Schlafen family member 11 (SLFN11), inhibits HIV protein translation in a codon-dependent fashion . SpecifiUpon translation, viral proteins depend on a variety of host enzymes that mediate post-translational modifications. These include phosphorylation, N- and O-linked glycosylation, acetylation, the attachment of hydrophobic groups for membrane localization , and many other processes that determine protein stability, localization, and activity. Consequently, modulation of these modifications may represent an efficient means of the host to interfere with viral replication. One post-translational modification that is targeted by host factors to suppress viral protein maturation is proteolytic cleavage. While many viral pathogens encode proteases to mediate proteolytic processing of their own (poly)proteins, most of them also exploit cellular proteases. One prominent example is the ubiquitously expressed host protease furin/PCSK3 that activates a variety of viral envelope glycoproteins by cleaving a poly-basic consensus motif (R-X-K/R-R↓). Among others, this comprises the envelope (Env) proteins of retroviruses such as HIV-1 , the hemIn addition to inhibiting normal post-translation modifications of viral proteins, infected cells may also ‘mark’ viral proteins to prevent them from exerting their functions. For example, ISGylation has been shown to negatively interfere with the stability, activity, and/or assembly of viral proteins , left. TIn summary, these examples illustrate that post-translational modifications represent an effective mechanism of the host to interfere with replication of diverse viral pathogens. However, protein-modifying enzymes frequently affect both viral and cellular proteins, since features generally distinguishing self from non-self proteins are missing. The induction of factors regulating post-translational modifications  upon viral infection may represent one means to limit potential harmful side effects on the host.Upon assembly of viral proteins and nucleic acids, enveloped progeny virions bud from cellular membranes. Depending on the virus species, budding takes place in cellular compartments  or specific virus-induced organelles. Not surprisingly, host factors interfering with membrane composition, transport, or curvature may affect virus budding. For example, the IFN-inducible protein viperin/RSAD2 exerts antiviral activity by interfering with cholesterol metabolism and, thus, lipid composition of membranes. Viperin has been shown to interact with farnesyl diphosphate synthase (FPPS), an enzyme essential for isoprenoid biosynthesis . While oAnother well-characterized and broadly active antiviral factor that targets membranes to inhibit virus release is tetherin/BST-2 . InsteadOverall, the current literature suggests that membrane-targeting antiviral factors have the potential to target several steps of the viral replication cycle including fusion, formation of membranous replication complexes, and budding. A clear discrimination between self and non-self membranes can hardly be achieved since viral membranes are always derived from host cell membranes. Nevertheless, some specificity may be conferred by targeting detergent-rich membrane microdomains that serve as entry and budding sites for several viruses or by detecting specific membrane curvatures.Some antiviral factors may exert their inhibitory activity even after successful budding and release of newly formed virions. As discussed above, this includes cellular proteins such as SERINC5 or IFITMs that are incorporated into progeny virions and impair their infectivity. Other cellular factors that are well known to impair virion infectivity, albeit at an even later stage, are members of the APOBEC3  family. APOBEC3 proteins are cytidine deaminases that interact with viral RNAs and are encapsidated into newly formed virions. They are best established as restriction factors of retroviruses and retrotransposons . HoweverHowever, APOBEC proteins introduce mutations not only in viral nucleic acids but also in cellular nucleic acids. In fact, the first example of mRNA editing observed in vertebrates was the C to U editing of apolipoprotein B (ApoB) mRNA by APOBEC1 . This edOne particular challenge in the discrimination of self from non-self is the recognition of endogenous retroviruses (ERVs) by sensors and effectors of the innate immune response. ERVs are fossils of once infectious retroviruses that make up about 5–8% of the human DNA. Their ancestors infected germ cells and integrated their proviral DNA into the host genome. While many integrated proviruses were lost during evolution, others got fixed in the population and are now inherited in a Mendelian manner. In many cases, these endogenous retroviral sequences are silenced by genetic and epigenetic mechanisms as well as antiviral factors to prevent detrimental effects of their activation and spread. Some ERVs, however, have been co-opted by the host and fulfill important physiological functions in vivo. Consequently, restriction factors targeting retroviral components need to discriminate between beneficial endogenous retroviruses and their harmful counterparts to limit detrimental side effects.cis-regulatory ERVs also help to mount an efficient immune response upon infection. Expression of the inflammasome component AIM2, for example, is enhanced by an ERV of the MER41 family , while only 1–2% encode for proteins . Furthercis-regulatory elements  as well as viral proteins (e.g. Env). Notably, the presence of repetitive viral elements also facilitates gene loss and duplication events and, thus, faster adaptation of the human organism to an ever-changing environment. Similarly, the mutagenic activity of antiviral factor such as APOBEC3 proteins may not always have detrimental effects but also facilitate adaptation of the host to environmental changes. Thus, targeting of cellular nucleic acids or proteins by antiviral factors may not necessarily be detrimental, but also help the host to regulate cellular processes, particularly in response to stress stimuli that induce the expression of antiviral proteins. Consequently, both the absence and presence of antiviral factors as well as endogenous viral elements may provide selection advantages to the host. At the end of the day, host organisms may have found a balance that allows them to efficiently fight off most of the viral pathogens they encounter, while tolerating a few drawbacks that may be associated with the activity of antiviral proteins. One interesting question is whether special features may allow some species to minimize adverse effects of innate immune mechanisms. For example, it has been suggested that the high body temperatures and metabolic rates achieved during flight promoted the evolution of reduced reaction to foreign and self-DNAs in bats viral sequences. This includes the co-option of virus-derived  in bats . Without in bats .While we can only speculate about how humans may have evolved in a world without viruses, there is one thing we can say for certain: the human organism has been shaped to a large extent by viruses. This is not only due to the presence of hundreds of thousands of endogenous retroviral sequences in our genome but also due to the consequence of the evolution of antiviral factors that have driven the evolution of the entire human genome.