Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor

Decoding the Transcriptional Response to Ischemic Stroke in Young and Aged Mouse Brain

Ischemic stroke is a well-recognized disease of aging, yet it is unclear how the age-dependent vulnerability occurs and what are the underlying mechanisms. To address these issues, we perform a comprehensive RNA-seq analysis of aging, ischemic stroke, and their interaction in 3- and 18-month-old mice. We assess differential gene expression across injury status and age, estimate cell type proportion changes, assay the results against a range of transcriptional signatures from the literature, and perform unsupervised co-expression analysis, identifying modules of genes with varying response to injury. We uncover downregulation of axonal and synaptic maintenance genetic program, and increased activation of type I interferon (IFN-I) signaling following stroke in aged mice. Together, these results paint a picture of ischemic stroke as a complex age-related disease and provide insights into interaction of aging and stroke on cellular and molecular level.

Single-cell RNA sequencing of cultured human endometrial CD140b+CD146+ perivascular cells highlights the importance of in vivo microenvironment

Background

Endometrial mesenchymal-like stromal/stem cells (eMSCs) have been proposed as adult stem cells contributing to endometrial regeneration. One set of perivascular markers (CD140b&CD146) has been widely used to enrich eMSCs. Although eMSCs are easily accessible for regenerative medicine and have long been studied, their cellular heterogeneity, relationship to primary counterpart, remains largely unclear.

Methods

In this study, we applied 10X genomics single-cell RNA sequencing (scRNA-seq) to cultured human CD140b⁺CD146⁺ endometrial perivascular cells (ePCs) from menstrual and secretory endometrium. We also analyzed publicly available scRNA-seq data of primary endometrium and performed transcriptome comparison between cultured ePCs and primary ePCs at single-cell level.

Results

Transcriptomic expression-based clustering revealed limited heterogeneity within cultured menstrual and secretory ePCs. A main subpopulation and a small stress-induced subpopulation were identified in secretory and menstrual ePCs. Cell identity analysis demonstrated the similar cellular composition in secretory and menstrual ePCs. Marker gene expression analysis showed that the main subpopulations identified from cultured secretory and menstrual ePCs simultaneously expressed genes marking mesenchymal stem cell (MSC), perivascular cell, smooth muscle cell, and stromal fibroblast. GO enrichment analysis revealed that genes upregulated in the main subpopulation enriched in actin filament organization, cellular division, etc., while genes upregulated in the small subpopulation enriched in extracellular matrix disassembly, stress response, etc. By comparing subpopulations of cultured ePCs to the publicly available primary endometrial cells, it was found that the main subpopulation identified from cultured ePCs was culture-unique which was unlike primary ePCs or primary endometrial stromal fibroblast cells.

Conclusion

In summary, these data for the first time provides a single-cell atlas of the cultured human CD140b⁺CD146⁺ ePCs. The identification of culture-unique relatively homogenous cell population of CD140b⁺CD146⁺ ePCs underscores the importance of in vivo microenvironment in maintaining cellular identity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13287-021-02354-1.

Single cell transcriptomics of primate sensory neurons identifies cell types associated with chronic pain

Distinct types of dorsal root ganglion sensory neurons may have unique contributions to chronic pain. Identification of primate sensory neuron types is critical for understanding the cellular origin and heritability of chronic pain. However, molecular insights into the primate sensory neurons are missing. Here we classify non-human primate dorsal root ganglion sensory neurons based on their transcriptome and map human pain heritability to neuronal types. First, we identified cell correlates between two major datasets for mouse sensory neuron types. Machine learning exposes an overall cross-species conservation of somatosensory neurons between primate and mouse, although with differences at individual gene level, highlighting the importance of primate data for clinical translation. We map genomic loci associated with chronic pain in human onto primate sensory neuron types to identify the cellular origin of chronic pain. Genome-wide associations for chronic pain converge on two different neuronal types distributed between pain disorders that display different genetic susceptibilities, suggesting both unique and shared mechanisms between different pain conditions.

The contribution of distinct types of dorsal root ganglion neurons to chronic pain is unclear. Here, the authors molecularly profile non-human primate sensory neurons and show that genome-wide associations converge on two neuronal types with different genetic susceptibilities for chronic pain.

Tracing cell-type evolution by cross-species comparison of cell atlases

Cell types are the basic building units of multicellular life, with extensive diversities. The evolution of cell types is a crucial layer of comparative cell biology but is thus far not comprehensively studied. We define a compendium of cell atlases using single-cell RNA-seq (scRNA-seq) data from seven animal species and construct a cross-species cell-type evolutionary hierarchy. We present a roadmap for the origin and diversity of major cell categories and find that muscle and neuron cells are conserved cell types. Furthermore, we identify a cross-species transcription factor (TF) repertoire that specifies major cell categories. Overall, our study reveals conservation and divergence of cell types during animal evolution, which will further expand the landscape of comparative genomics.

Spatiotemporal strategies to identify aggressive biology in precancerous breast biopsies

Over 90% of breast cancer is cured; yet there remain highly aggressive breast cancers that develop rapidly and are extremely difficult to treat, much less prevent. Breast cancers that rapidly develop between breast image screening are called "interval cancers." The efforts of our team focus on identifying multiscale integrated strategies to identify biologically aggressive precancerous breast lesions. Our goal is to identify spatiotemporal changes that occur prior to development of interval breast cancers. To accomplish this requires integration of new technology. Our team has the ability to perform single cell in situ transcriptional profiling, noncontrast biological imaging, mathematical analysis, and nanoscale evaluation of receptor organization and signaling. These technological innovations allow us to start to identify multidimensional spatial and temporal relationships that drive the transition from biologically aggressive precancer to biologically aggressive interval breast cancer. This article is categorized under: Cancer > Computational Models Cancer > Molecular and Cellular Physiology Cancer > Genetics/Genomics/Epigenetics.

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

Single-cell sequencing technologies, including transcriptomic and epigenomic assays, are transforming our understanding of the cellular building blocks of neural circuits. By directly measuring multiple molecular signatures in thousands to millions of individual cells, single-cell sequencing methods can comprehensively characterize the diversity of brain cell types. These measurements uncover gene regulatory mechanisms that shape cellular identity and provide insight into developmental and evolutionary relationships between brain cell populations. Single-cell sequencing data can aid the design of tools for targeted functional studies of brain circuit components, linking molecular signatures with anatomy, connectivity, morphology, and physiology. Here, we discuss the fundamental principles of single-cell transcriptome and epigenome sequencing, integrative computational analysis of the data, and key applications in neuroscience.

Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery

Crop productivity depends on activity of meristems that produce optimized plant architectures, including that of the maize ear. A comprehensive understanding of development requires insight into the full diversity of cell types and developmental domains and the gene networks required to specify them. Until now, these were identified primarily by morphology and insights from classical genetics, which are limited by genetic redundancy and pleiotropy. Here, we investigated the transcriptional profiles of 12,525 single cells from developing maize ears. The resulting developmental atlas provides a single-cell RNA sequencing (scRNA-seq) map of an inflorescence. We validated our results by mRNA in situ hybridization and by fluorescence-activated cell sorting (FACS) RNA-seq, and we show how these data may facilitate genetic studies by predicting genetic redundancy, integrating transcriptional networks, and identifying candidate genes associated with crop yield traits.

Transcriptomic Profiling of Human Pluripotent Stem Cell-derived Retinal Pigment Epithelium over Time

Human pluripotent stem cell (hPSC)-derived progenies are immature versions of cells, presenting a potential limitation to the accurate modelling of diseases associated with maturity or age. Hence, it is important to characterise how closely cells used in culture resemble their native counterparts. In order to select appropriate time points of retinal pigment epithelium (RPE) cultures that reflect native counterparts, we characterised the transcriptomic profiles of the hPSC-derived RPE cells from 1- and 12-month cultures. We differentiated the human embryonic stem cell line H9 into RPE cells, performed single-cell RNA-sequencing of a total of 16,576 cells to assess the molecular changes of the RPE cells across these two culture time points. Our results indicate the stability of the RPE transcriptomic signature, with no evidence of an epithelial–mesenchymal transition, and with the maturing populations of the RPE observed with time in culture. Assessment of Gene Ontology pathways revealed that as the cultures age, RPE cells upregulate expression of genes involved in metal binding and antioxidant functions. This might reflect an increased ability to handle oxidative stress as cells mature. Comparison with native human RPE data confirms a maturing transcriptional profile of RPE cells in culture. These results suggest that long-term in vitro culture of RPE cells allows the modelling of specific phenotypes observed in native mature tissues. Our work highlights the transcriptional landscape of hPSC-derived RPE cells as they age in culture, which provides a reference for native and patient samples to be benchmarked against.

Diversification of molecularly defined myenteric neuron classes revealed by single-cell RNA sequencing

Autonomous regulation of the intestine requires the combined activity of functionally distinct neurons of the enteric nervous system (ENS). However, the variety of enteric neuron types and how they emerge during development remain largely unknown. Here, we define a molecular taxonomy of twelve enteric neuron classes within the myenteric plexus of the mouse small intestine using single cell RNA-sequencing. We present cell-cell communication features, histochemical markers for motor, sensory, and interneurons together with transgenic tools for class-specific targeting. Transcriptome analysis of embryonic ENS uncovers a novel principle of neuronal diversification, where two neuron classes arise through a binary neurogenic branching, and all other identities emerge through subsequent post-mitotic differentiation. We identify generic and class-specific transcriptional regulators and functionally connect Pbx3 to a post-mitotic fate transition. Our results offer a conceptual and molecular resource for dissecting ENS circuits, and predicting key regulators for directed differentiation of distinct enteric neuron classes.

Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) is a popular and powerful technology that allows you to profile the whole transcriptome of a large number of individual cells. However, the analysis of the large volumes of data generated from these experiments requires specialized statistical and computational methods. Here we present an overview of the computational workflow involved in processing scRNA-seq data. We discuss some of the most common tasks and the tools available for addressing central biological questions. In this article and our companion website ( https://scrnaseq-course.cog.sanger.ac.uk/website/index.html ), we provide guidelines regarding best practices for performing computational analyses. This tutorial provides a hands-on guide for experimentalists interested in analyzing their data as well as an overview for bioinformaticians seeking to develop new computational methods.

A community-based transcriptomics classification and nomenclature of neocortical cell types

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Single-cell Proteomics: Progress and Prospects

MS-based proteome profiling has become increasingly comprehensive and quantitative, yet a persistent shortcoming has been the relatively large samples required to achieve an in-depth measurement. Such bulk samples, typically comprising thousands of cells or more, provide a population average and obscure important cellular heterogeneity. Single-cell proteomics capabilities have the potential to transform biomedical research and enable understanding of biological systems with a new level of granularity. Recent advances in sample processing, separations and MS instrumentation now make it possible to quantify >1000 proteins from individual mammalian cells, a level of coverage that required an input of thousands of cells just a few years ago. This review discusses important factors and parameters that should be optimized across the workflow for single-cell and other low-input measurements. It also highlights recent developments that have advanced the field and opportunities for further development.

Transcriptional priming as a conserved mechanism of lineage diversification in the developing mouse and human neocortex

How the rich variety of neurons in the nervous system arises from neural stem cells is not well understood. Using single-cell RNA-sequencing and in vivo confirmation, we uncover previously unrecognized neural stem and progenitor cell diversity within the fetal mouse and human neocortex, including multiple types of radial glia and intermediate progenitors. We also observed that transcriptional priming underlies the diversification of a subset of ventricular radial glial cells in both species; genetic fate mapping confirms that the primed radial glial cells generate specific types of basal progenitors and neurons. The different precursor lineages therefore diversify streams of cell production in the developing murine and human neocortex. These data show that transcriptional priming is likely a conserved mechanism of mammalian neural precursor lineage specialization.

Evaluation of single-cell classifiers for single-cell RNA sequencing data sets

Single-cell RNA sequencing (scRNA-seq) has been rapidly developing and widely applied in biological and medical research. Identification of cell types in scRNA-seq data sets is an essential step before in-depth investigations of their functional and pathological roles. However, the conventional workflow based on clustering and marker genes is not scalable for an increasingly large number of scRNA-seq data sets due to complicated procedures and manual annotation. Therefore, a number of tools have been developed recently to predict cell types in new data sets using reference data sets. These methods have not been generally adapted due to a lack of tool benchmarking and user guidance. In this article, we performed a comprehensive and impartial evaluation of nine classification software tools specifically designed for scRNA-seq data sets. Results showed that Seurat based on random forest, SingleR based on correlation analysis and CaSTLe based on XGBoost performed better than others. A simple ensemble voting of all tools can improve the predictive accuracy. Under nonideal situations, such as small-sized and class-imbalanced reference data sets, tools based on cluster-level similarities have superior performance. However, even with the function of assigning 'unassigned' labels, it is still challenging to catch novel cell types by solely using any of the single-cell classifiers. This article provides a guideline for researchers to select and apply suitable classification tools in their analysis workflows and sheds some lights on potential direction of future improvement on classification tools.

A single-cell survey of cellular hierarchy in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy and has a prognosis that varies with its genetic complexity. However, there has been no appropriate integrative analysis on the hierarchy of different AML subtypes.

Methods

Using Microwell-seq, a high-throughput single-cell mRNA sequencing platform, we analyzed the cellular hierarchy of bone marrow samples from 40 patients and 3 healthy donors. We also used single-cell single-molecule real-time (SMRT) sequencing to investigate the clonal heterogeneity of AML cells.

Results

From the integrative analysis of 191727 AML cells, we established a single-cell AML landscape and identified an AML progenitor cell cluster with novel AML markers. Patients with ribosomal protein high progenitor cells had a low remission rate. We deduced two types of AML with diverse clinical outcomes. We traced mitochondrial mutations in the AML landscape by combining Microwell-seq with SMRT sequencing. We propose the existence of a phenotypic “cancer attractor” that might help to define a common phenotype for AML progenitor cells. Finally, we explored the potential drug targets by making comparisons between the AML landscape and the Human Cell Landscape.

Conclusions

We identified a key AML progenitor cell cluster. A high ribosomal protein gene level indicates the poor prognosis. We deduced two types of AML and explored the potential drug targets. Our results suggest the existence of a cancer attractor.

Mapping a mammalian adult adrenal gland hierarchy across species by microwell-seq

Recently, single-cell RNA-seq technologies have been rapidly updated, leading to a revolution in biology. We previously developed Microwell-seq, a cost-effective and high-throughput single cell RNA sequencing(scRNA-seq) method with a very simple device. Most cDNA libraries are sequenced using an expensive Illumina platform. Here, we present the first report showing combined Microwell-seq and BGI MGISEQ2000, a less expensive sequencing platform, to profile the whole transcriptome of 11,883 individual mouse adult adrenal gland cells and identify 18 transcriptionally distinct clusters. Moreover, we performed a single-cell comparative analysis of human and mouse adult adrenal glands to reveal the conserved genetic networks in these mammalian systems. These results provide new insights into the sophisticated adrenal gland hierarchy and provide a benchmark, low-cost strategy for high-throughput single-cell RNA study.

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Advances in single-cell RNA sequencing (scRNA-seq) technologies in the past 10 years have had a transformative effect on biomedical research, enabling the profiling and analysis of the transcriptomes of single cells at unprecedented resolution and throughput. Specifically, scRNA-seq has facilitated the identification of novel or rare cell types, the analysis of single-cell trajectory construction and stem or progenitor cell differentiation, and the comparison of healthy and disease-related tissues at single-cell resolution. These applications have been critical in advances in cardiovascular research in the past decade as evidenced by the generation of cell atlases of mammalian heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and stem or progenitor cell differentiation. In this Review, we summarize the currently available scRNA-seq technologies and analytical tools and discuss the latest findings using scRNA-seq that have substantially improved our knowledge on the development of the cardiovascular system and the mechanisms underlying cardiovascular diseases. Furthermore, we examine emerging strategies that integrate multimodal single-cell platforms, focusing on future applications in cardiovascular precision medicine that use single-cell omics approaches to characterize cell-specific responses to drugs or environmental stimuli and to develop effective patient-specific therapeutics.

Impact of Local Alloimmunity and Recipient Cells in Transplant Arteriosclerosis

RATIONALE

Transplant arteriosclerosis is the major limitation to long-term survival of solid organ transplantation. Although both immune and nonimmune cells have been suggested to contribute to this process, the complex cellular heterogeneity within the grafts, and the underlying mechanisms regulating the disease progression remain largely uncharacterized.

OBJECTIVE

We aimed to delineate the cellular heterogeneity within the allografts, and to explore possible mechanisms underlying this process.

METHODS AND RESULTS

Here, we reported the transcriptional profiling of 11 868 cells in a mouse model of transplant arteriosclerosis by single-cell RNA sequencing. Unbiased clustering analyses identified 21 cell clusters at different stages of diseases, and focused analysis revealed several previously unknown subpopulations enriched in the allografts. Interestingly, we found evidence of the local formation of tertiary lymphoid tissues and suggested a possible local modulation of alloimmune responses within the grafts. Intercellular communication analyses uncovered a potential role of several ligands and receptors, including Ccl21a and Cxcr3, in regulating lymphatic endothelial cell-induced early chemotaxis and infiltration of immune cells. In vivo mouse experiments confirmed the therapeutic potential of CCL21 and CXCR3 neutralizing antibodies in transplant arteriosclerosis. Combinational use of genetic lineage tracing and single-cell techniques further indicate the infiltration of host-derived c-Kit⁺ stem cells as heterogeneous populations in the allografts. Finally, we compared the immune response between mouse allograft and atherosclerosis models in single-cell RNA-seq analysis. By analyzing susceptibility genes of disease traits, we also identified several cell clusters expressing genes associated with disease risk.

CONCLUSIONS

Our study provides a transcriptional and cellular landscape of transplant arteriosclerosis, which could be fundamental to understanding the initiation and progression of this disease. CCL21/CXCR3 was also identified as important regulators of immune response and may serve as potential therapeutic targets in disease treatment.

Computational Methods for Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed.

An Atlas of Vagal Sensory Neurons and Their Molecular Specialization

Sensory functions of the vagus nerve are critical for conscious perceptions and for monitoring visceral functions in the cardio-pulmonary and gastrointestinal systems. Here, we present a comprehensive identification, classification, and validation of the neuron types in the neural crest (jugular) and placode (nodose) derived vagal ganglia by single-cell RNA sequencing (scRNA-seq) transcriptomic analysis. Our results reveal major differences between neurons derived from different embryonic origins. Jugular neurons exhibit fundamental similarities to the somatosensory spinal neurons, including major types, such as C-low threshold mechanoreceptors (C-LTMRs), A-LTMRs, Aδ-nociceptors, and cold-, and mechano-heat C-nociceptors. In contrast, the nodose ganglion contains 18 distinct types dedicated to surveying the physiological state of the internal body. Our results reveal a vast diversity of vagal neuron types, including many previously unanticipated types, as well as proposed types that are consistent with chemoreceptors, nutrient detectors, baroreceptors, and stretch and volume mechanoreceptors of the respiratory, gastrointestinal, and cardiovascular systems.

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A comprehensive molecular identification of neuronal types in vagal ganglion complex

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Prdm12⁺ jugular ganglion neurons share features with spinal somatosensory neurons

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Phox2b⁺ viscerosensory nodose neurons are molecularly versatile and highly specialized

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Nodose neuron types are consistent with chemo-, baro-, stretch-, tension-, and volume-sensors

Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

Medical Treatment of Lung Cancer: Can Immune Cells Predict the Response? A Systematic Review

The landscape for medical treatment of lung cancer has irreversibly changed since the development of immuno-oncology (IO). Yet, while immune checkpoint blockade (ICB) revealed that T lymphocytes play a major role in lung cancer, the precise dynamic of innate and adaptive immune cells induced by anticancer treatments including chemotherapy, targeted therapy, and/or ICB is poorly understood. In lung cancer, studies evaluating specific immune cell populations as predictors of response to medical treatment are scarce, and knowledge is fragmented. Here, we review the different techniques allowing the detection of immune cells in the tumor and blood (multiplex immunohistochemistry and immunofluorescence, RNA-seq, DNA methylation pattern, mass cytometry, functional tests). In addition, we present data that consider different baseline immune cell populations as predictors of response to medical treatments of lung cancer. We also review the potential for assessing dynamic changes in cell populations during treatment as a biomarker. As powerful tools for immune cell detection and data analysis are available, clinicians and researchers could increase understanding of mechanisms of efficacy and resistance in addition to identifying new targets for IO by developing translational studies that decipher the role of different immune cell populations during lung cancer treatments.

Molecular design of hypothalamus development

A wealth of specialized neuroendocrine command systems intercalated within the hypothalamus control the most fundamental physiological needs in vertebrates1,2. Nevertheless, we lack a developmental blueprint that integrates the molecular determinants of neuronal and glial diversity along temporal and spatial scales of hypothalamus development3. Here we combine single-cell RNA sequencing of 51,199 mouse cells of ectodermal origin, gene regulatory network (GRN) screens in conjunction with genome-wide association study-based disease phenotyping, and genetic lineage reconstruction to show that nine glial and thirty-three neuronal subtypes are generated by mid-gestation under the control of distinct GRNs. Combinatorial molecular codes that arise from neurotransmitters, neuropeptides and transcription factors are minimally required to decode the taxonomical hierarchy of hypothalamic neurons. The differentiation of γ-aminobutyric acid (GABA) and dopamine neurons, but not glutamate neurons, relies on quasi-stable intermediate states, with a pool of GABA progenitors giving rise to dopamine cells4. We found an unexpected abundance of chemotropic proliferation and guidance cues that are commonly implicated in dorsal (cortical) patterning5 in the hypothalamus. In particular, loss of SLIT-ROBO signalling impaired both the production and positioning of periventricular dopamine neurons. Overall, we identify molecular principles that shape the developmental architecture of the hypothalamus and show how neuronal heterogeneity is transformed into a multimodal neural unit to provide virtually infinite adaptive potential throughout life.

Multimodal Analysis of Cell Types in a Hypothalamic Node Controlling Social Behavior

The ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) contains ∼4,000 neurons that project to multiple targets and control innate social behaviors including aggression and mounting. However, the number of cell types in VMHvl and their relationship to connectivity and behavioral function are unknown. We performed single-cell RNA sequencing using two independent platforms-SMART-seq (∼4,500 neurons) and 10x (∼78,000 neurons)-and investigated correspondence between transcriptomic identity and axonal projections or behavioral activation, respectively. Canonical correlation analysis (CCA) identified 17 transcriptomic types (T-types), including several sexually dimorphic clusters, the majority of which were validated by seqFISH. Immediate early gene analysis identified T-types exhibiting preferential responses to intruder males versus females but only rare examples of behavior-specific activation. Unexpectedly, many VMHvl T-types comprise a mixed population of neurons with different projection target preferences. Overall our analysis revealed that, surprisingly, few VMHvl T-types exhibit a clear correspondence with behavior-specific activation and connectivity.

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3′ libraries consisting of over 70 000 cells generated on the 10× Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

Single-Cell RNA Sequencing and Its Combination with Protein and DNA Analyses

Heterogeneity in cell populations poses a significant challenge for understanding complex cell biological processes. The analysis of cells at the single-cell level, especially single-cell RNA sequencing (scRNA-seq), has made it possible to comprehensively dissect cellular heterogeneity and access unobtainable biological information from bulk analysis. Recent efforts have combined scRNA-seq profiles with genomic or proteomic data, and show added value in describing complex cellular heterogeneity than transcriptome measurements alone. With the rising demand for scRNA-seq for biomedical and clinical applications, there is a strong need for a timely and comprehensive review on the scRNA-seq technologies and their potential biomedical applications. In this review, we first discuss the latest state of development by detailing each scRNA-seq technology, including both conventional and microfluidic technologies. We then summarize their advantages and limitations along with their biomedical applications. The efforts of integrating the transcriptome profile with highly multiplexed proteomic and genomic data are thoroughly reviewed with results showing the integrated data being more informative than transcriptome data alone. Lastly, the latest progress toward commercialization, the remaining challenges, and future perspectives on the development of scRNA-seq technologies are briefly discussed.

Construction of a human cell landscape at single-cell level

Single-cell analysis is a valuable tool for dissecting cellular heterogeneity in complex systems¹. However, a comprehensive single-cell atlas has not been achieved for humans. Here we use single-cell mRNA sequencing to determine the cell-type composition of all major human organs and construct a scheme for the human cell landscape (HCL). We have uncovered a single-cell hierarchy for many tissues that have not been well characterized. We established a 'single-cell HCL analysis' pipeline that helps to define human cell identity. Finally, we performed a single-cell comparative analysis of landscapes from human and mouse to identify conserved genetic networks. We found that stem and progenitor cells exhibit strong transcriptomic stochasticity, whereas differentiated cells are more distinct. Our results provide a useful resource for the study of human biology.

Expression and Localization of Kcne2 in the Vertebrate Retina

Purpose

To characterize the retinal expression and localization of Kcne2, an ancillary (β) ion-channel subunit with an important role in fine-tuning cellular excitability.

Methods

We analyzed available single-cell transcriptome data from tens of thousands of murine retinal cells for cell-type-specific expression of Kcne2 using state-of-the-art bioinformatics techniques. This evidence at the transcriptome level was complemented with a comprehensive immunohistochemical characterization of mouse retina (C57BL/6, ages 8-12 weeks) employing co-labeling techniques and cell-type-specific antibody markers. We furthermore examined how conserved the Kcne2 localization pattern in the retina was across species by performing immunostaining on zebrafish, cowbird, sheep, mice, and macaque.

Results

Kcne2 is distinctly expressed in cone photoreceptors and rod bipolar cells. At a subcellular level, the bulk of Kcne2 immunoreactivity can be observed in the outer plexiform layer. Here, it localizes into cone pedicles and likely the postsynaptic membrane of the rod bipolar cells. Thus, the vast majority of Kcne2 immunoreactivity is observed in a thin band in the outer plexiform layer. In addition to this, faint Kcne2 immunoreactivity can also be observed in cone inner segments and the somata of a small subset of cone ON bipolar cells. Strikingly, the localization of Kcne2 in the outer plexiform layer was preserved among all of the species studied, spanning at least 300 million years of evolution of the vertebrate kingdom.

Conclusions

The data we present here suggest an important and specific role for Kcne2 in the highly specialized photoreceptor-bipolar cell synapse.

Distinguishing cell phenotype using cell epigenotype

The relationship between microscopic observations and macroscopic behavior is a fundamental open question in biophysical systems. Here, we develop a unified approach that-in contrast with existing methods-predicts cell type from macromolecular data even when accounting for the scale of human tissue diversity and limitations in the available data. We achieve these benefits by applying a k-nearest-neighbors algorithm after projecting our data onto the eigenvectors of the correlation matrix inferred from many observations of gene expression or chromatin conformation. Our approach identifies variations in epigenotype that affect cell type, thereby supporting the cell-type attractor hypothesis and representing the first step toward model-independent control strategies in biological systems.

Single cell and genetic analyses reveal conserved populations and signaling mechanisms of gastrointestinal stromal niches

Stomach and intestinal stem cells are located in discrete niches called the isthmus and crypt, respectively. Recent studies have demonstrated a surprisingly conserved role for Wnt signaling in gastrointestinal development. Although intestinal stromal cells secrete Wnt ligands to promote stem cell renewal, the source of stomach Wnt ligands is still unclear. Here, by performing single cell analysis, we identify gastrointestinal stromal cell populations with transcriptome signatures that are conserved between the stomach and intestine. In close proximity to epithelial cells, these perictye-like cells highly express telocyte and pericyte markers as well as Wnt ligands, and they are enriched for Hh signaling. By analyzing mice activated for Hh signaling, we show a conserved mechanism of GLI2 activation of Wnt ligands. Moreover, genetic inhibition of Wnt secretion in perictye-like stromal cells or stromal cells more broadly demonstrates their essential roles in gastrointestinal regeneration and development, respectively, highlighting a redundancy in gastrointestinal stem cell niches.

Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data

In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.

scReClassify: post hoc cell type classification of single-cell rNA-seq data

Background

Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling.

Results

Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types.

Conclusions

scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify

LAmbDA: label ambiguous domain adaptation dataset integration reduces batch effects and improves subtype detection

MOTIVATION

Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes in multiple tissues and species. Methods are increasingly needed across datasets and species to (i) remove systematic biases, (ii) model multiple datasets with ambiguous labels and (iii) classify cells and map cell type labels. However, most methods only address one of these problems on broad cell types or simulated data using a single model type. It is also important to address higher-resolution cellular subtypes, subtype labels from multiple datasets, models trained on multiple datasets simultaneously and generalizability beyond a single model type.

RESULTS

We developed a species- and dataset-independent transfer learning framework (LAmbDA) to train models on multiple datasets (even from different species) and applied our framework on simulated, pancreas and brain scRNA-seq experiments. These models mapped corresponding cell types between datasets with inconsistent cell subtype labels while simultaneously reducing batch effects. We achieved high accuracy in labeling cellular subtypes (weighted accuracy simulated 1 datasets: 90%; simulated 2 datasets: 94%; pancreas datasets: 88% and brain datasets: 66%) using LAmbDA Feedforward 1 Layer Neural Network with bagging. This method achieved higher weighted accuracy in labeling cellular subtypes than two other state-of-the-art methods, scmap and CaSTLe in brain (66% versus 60% and 32%). Furthermore, it achieved better performance in correctly predicting ambiguous cellular subtype labels across datasets in 88% of test cases compared with CaSTLe (63%), scmap (50%) and MetaNeighbor (50%). LAmbDA is model- and dataset-independent and generalizable to diverse data types representing an advance in biocomputing.

AVAILABILITY AND IMPLEMENTATION

github.com/tsteelejohnson91/LAmbDA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-Throughput Mapping of Long-Range Neuronal Projection Using In Situ Sequencing

Understanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single-neuron resolution and high throughput, a challenging combination with conventional methods. Here, we introduce barcoded anatomy resolved by sequencing (BARseq), a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3,579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (intratelencephalic [IT], pyramidal tract-like [PT-like], and corticothalamic [CT]) of projection neurons. In depth analysis uncovered a projection type restricted almost exclusively to transcriptionally defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.

Self-assembling manifolds in single-cell RNA sequencing data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.

Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction

Coordinated activity-induced transcriptional changes across multiple neuron subtypes of the prefrontal cortex (PFC) play a pivotal role in encoding and regulating major cognitive behaviors. Yet, the specific transcriptional programs in each neuron subtype remain unknown. Using single-cell RNA sequencing (scRNA-seq), here we comprehensively classify all unique cell subtypes in the PFC. We analyze transcriptional dynamics of each cell subtype under a naturally adaptive and an induced condition. Adaptive changes during adolescence (between P21 and P60), a highly dynamic phase of postnatal neuroplasticity, profoundly impacted transcription in each neuron subtype, including cell type-specific regulation of genes implicated in major neuropsychiatric disorders. On the other hand, an induced plasticity evoked by chronic cocaine addiction resulted in progressive transcriptional changes in multiple neuron subtypes and became most pronounced upon prolonged drug withdrawal. Our findings lay a foundation for understanding cell type-specific postnatal transcriptional dynamics under normal PFC function and in neuropsychiatric disease states.

A comparison of automatic cell identification methods for single-cell RNA sequencing data

BACKGROUND

Single-cell transcriptomics is rapidly advancing our understanding of the cellular composition of complex tissues and organisms. A major limitation in most analysis pipelines is the reliance on manual annotations to determine cell identities, which are time-consuming and irreproducible. The exponential growth in the number of cells and samples has prompted the adaptation and development of supervised classification methods for automatic cell identification.

RESULTS

Here, we benchmarked 22 classification methods that automatically assign cell identities including single-cell-specific and general-purpose classifiers. The performance of the methods is evaluated using 27 publicly available single-cell RNA sequencing datasets of different sizes, technologies, species, and levels of complexity. We use 2 experimental setups to evaluate the performance of each method for within dataset predictions (intra-dataset) and across datasets (inter-dataset) based on accuracy, percentage of unclassified cells, and computation time. We further evaluate the methods' sensitivity to the input features, number of cells per population, and their performance across different annotation levels and datasets. We find that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets with overlapping classes or deep annotations. The general-purpose support vector machine classifier has overall the best performance across the different experiments.

CONCLUSIONS

We present a comprehensive evaluation of automatic cell identification methods for single-cell RNA sequencing data. All the code used for the evaluation is available on GitHub ( https://github.com/tabdelaal/scRNAseq_Benchmark ). Additionally, we provide a Snakemake workflow to facilitate the benchmarking and to support the extension of new methods and new datasets.

The diversity of GABAergic neurons and neural communication elements

The phenotypic diversity of cortical GABAergic neurons is probably necessary for their functional versatility in shaping the spatiotemporal dynamics of neural circuit operations underlying cognition. Deciphering the logic of this diversity requires comprehensive analysis of multi-modal cell features and a framework of neuronal identity that reflects biological mechanisms and principles. Recent high-throughput single-cell analyses have generated unprecedented data sets characterizing the transcriptomes, morphology and electrophysiology of interneurons. We posit that cardinal interneuron types can be defined by their synaptic communication properties, which are encoded in key transcriptional signatures. This conceptual framework integrates multi-modal cell features, captures neuronal input-output properties fundamental to circuit operation and may advance understanding of the appropriate granularity of neuron types, towards a biologically grounded and operationally useful interneuron taxonomy.

Conserved cell types with divergent features in human versus mouse cortex

Elucidating the cellular architecture of the human cerebral cortex is central to understanding our cognitive abilities and susceptibility to disease. Here we used single-nucleus RNA-sequencing analysis to perform a comprehensive study of cell types in the middle temporal gyrus of human cortex. We identified a highly diverse set of excitatory and inhibitory neuron types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to similar mouse cortex single-cell RNA-sequencing datasets revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of properties of human cell types. Despite this general conservation, we also found extensive differences between homologous human and mouse cell types, including marked alterations in proportions, laminar distributions, gene expression and morphology. These species-specific features emphasize the importance of directly studying human brain.

BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes

To fully utilize the power of single-cell RNA sequencing (scRNA-seq) technologies for identifying cell lineages and bona fide transcriptional signals, it is necessary to combine data from multiple experiments. We present BERMUDA (Batch Effect ReMoval Using Deep Autoencoders), a novel transfer-learning-based method for batch effect correction in scRNA-seq data. BERMUDA effectively combines different batches of scRNA-seq data with vastly different cell population compositions and amplifies biological signals by transferring information among batches. We demonstrate that BERMUDA outperforms existing methods for removing batch effects and distinguishing cell types in multiple simulated and real scRNA-seq datasets.

SingleCellNet: A Computational Tool to Classify Single Cell RNA-Seq Data Across Platforms and Across Species

Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step in the analysis of single-cell RNA-seq data is to hypothesize the identity of each cell. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single-cell RNA-seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single-cell RNA-seq data in comparison to reference single-cell RNA-seq data. SingleCellNet compares favorably to other methods in sensitivity and specificity, and it is able to classify across platforms and species. We highlight SingleCellNet's utility by classifying previously undetermined cells, and by assessing the outcome of a cell fate engineering experiment.

Challenges in unsupervised clustering of single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters.

CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data

We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.

Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity

The trillions of cells in the human body can be viewed as elementary but essential biological units that achieve different body states, but the low resolution of previous cell isolation and measurement approaches limits our understanding of the cell-specific molecular profiles. The recent establishment and rapid growth of single-cell sequencing technology has facilitated the identification of molecular profiles of heterogeneous cells, especially on the transcription level of single cells [single-cell RNA sequencing (scRNA-seq)]. As a novel method, the robustness of scRNA-seq under changing conditions will determine its practical potential in major research programs and clinical applications. In this review, we first briefly presented the scRNA-seq-related methods from the point of view of experiments and computation. Then, we compared several state-of-the-art scRNA-seq analysis frameworks mainly by analyzing their performance robustness on independent scRNA-seq datasets for the same complex disease. Finally, we elaborated on our hypothesis on consensus scRNA-seq analysis and summarized the potential indicative and predictive roles of individual cells in understanding disease heterogeneity by single-cell technologies.

Perspectives on defining cell types in the brain

The diversity of brain cell types was one of the earliest observations in modern neuroscience and continues to be one of the central concerns of current neuroscience research. Despite impressive recent progress, including single cell transcriptome and epigenome profiling as well as anatomical methods, we still lack a complete census or taxonomy of brain cell types. We argue this is due partly to the conceptual difficulty in defining a cell type. By considering the biological drivers of cell identity, such as networks of genes and gene regulatory elements, we propose a definition of cell type that emphasizes self-stabilizing regulation. We explore the predictions and hypotheses that arise from this definition. Integration of data from multiple modalities, including molecular profiling of genes and gene products, epigenetic landscape, cellular morphology, connectivity, and physiology, will be essential for a meaningful and broadly useful definition of brain cell types.

Single cell RNA-sequencing: replicability of cell types

Recent technical advances have enabled transcriptomics experiments at an unprecedented scale, and single-cell profiles from neural tissue are accumulating rapidly. There has been considerable effort to use these profiles to understand cell diversity, primarily through unsupervised clustering and differential expression analysis. However, current practices to validate these findings vary. In this review, we describe recent efforts to evaluate clusters from single-cell RNA-sequencing data, and provide a framework for considering current evidence and practices in terms of their capacity to establish principles of cell biology. Single-cell RNA-sequencing has already transformed neuroscience. By facilitating detailed comparative and genetic perturbation analyses, it may provide the tools to uncover fundamental mechanisms of neural diversity throughout the tree of life.

Efficient integration of heterogeneous single-cell transcriptomes using Scanorama

Integration of single-cell RNA sequencing (scRNA-seq) data from multiple experiments, laboratories and technologies can uncover biological insights, but current methods for scRNA-seq data integration are limited by a requirement for datasets to derive from functionally similar cells. We present Scanorama, an algorithm that identifies and merges the shared cell types among all pairs of datasets and accurately integrates heterogeneous collections of scRNA-seq data. We applied Scanorama to integrate and remove batch effects across 105,476 cells from 26 diverse scRNA-seq experiments representing 9 different technologies. Scanorama is sensitive to subtle temporal changes within the same cell lineage, successfully integrating functionally similar cells across time series data of CD14+ monocytes at different stages of differentiation into macrophages. Finally, we show that Scanorama is orders of magnitude faster than existing techniques and can integrate a collection of 1,095,538 cells in just ~9 h.

A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies

The recently developed droplet-based single-cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we develop a Bayesian mixture model for single-cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulation studies and applications of BAMM-SC to in-house experimental scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrate that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals.

With the development of large scale single cell RNA-seq technology, population-scale scRNA-seq studies are emerging. Here, the authors develop BAMM-SC, a tool for clustering droplet-based scRNA-seq data from multiple individuals simultaneously.