Single-cell Gene Expression Analysis Research Articles

Spatial and single-cell transcriptome analysis reveals changes in gene expression in response to drug perturbation in rat kidney.

The kidney is a complex organ that consists of various types of cells. It is occasionally difficult to resolve molecular alterations and possible perturbations that the kidney experiences due to drug-induced damage. In this study, we performed spatial and single-cell transcriptome analysis of rat kidneys and constructed a precise rat renal cell atlas with spatial information. Using the constructed catalogue, we were able to characterize cells of several minor populations, such as macula densa or juxtaglomerular cells. Further inspection of the spatial gene expression data allowed us to identify the upregulation of genes involved in the renin regulating pathway in losartan-treated populations. Losartan is an angiotensin II receptor antagonist drug, and the observed upregulation of the renin pathway-related genes could be due to feedback from the hypotensive action of the drug. Furthermore, we found spatial heterogeneity in the response to losartan among the glomeruli. These results collectively indicate that integrated single-cell and spatial gene expression analysis is a powerful approach to reveal the detailed associations between the different cell types spanning the complicated renal compartments.

Immunopathological signatures in multisystem inflammatory syndrome in children and pediatric COVID-19.

Pediatric Coronavirus Disease 2019 (pCOVID-19) is rarely severe; however, a minority of children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) might develop multisystem inflammatory syndrome in children (MIS-C), with substantial morbidity. In this longitudinal multi-institutional study, we applied multi-omics (analysis of soluble biomarkers, proteomics, single-cell gene expression and immune repertoire analysis) to profile children with COVID-19 (n = 110) and MIS-C (n = 76), along with pediatric healthy controls (pHCs; n = 76). pCOVID-19 was characterized by robust type I interferon (IFN) responses, whereas prominent type II IFN-dependent and NF-κB-dependent signatures, matrisome activation and increased levels of circulating spike protein were detected in MIS-C, with no correlation with SARS-CoV-2 PCR status around the time of admission. Transient expansion of TRBV11-2 T cell clonotypes in MIS-C was associated with signatures of inflammation and T cell activation. The association of MIS-C with the combination of HLA A*02, B*35 and C*04 alleles suggests genetic susceptibility. MIS-C B cells showed higher mutation load than pCOVID-19 and pHC. These results identify distinct immunopathological signatures in pCOVID-19 and MIS-C that might help better define the pathophysiology of these disorders and guide therapy.

Single-cell transcriptomics of human iPSC differentiation dynamics reveal a core molecular network of Parkinson\u2019s disease

Parkinson’s disease (PD) is the second-most prevalent neurodegenerative disorder, characterized by the loss of dopaminergic neurons (mDA) in the midbrain. The underlying mechanisms are only partly understood and there is no treatment to reverse PD progression. Here, we investigated the disease mechanism using mDA neurons differentiated from human induced pluripotent stem cells (hiPSCs) carrying the ILE368ASN mutation within the PINK1 gene, which is strongly associated with PD. Single-cell RNA sequencing (RNAseq) and gene expression analysis of a PINK1-ILE368ASN and a control cell line identified genes differentially expressed during mDA neuron differentiation. Network analysis revealed that these genes form a core network, members of which interact with all known 19 protein-coding Parkinson’s disease-associated genes. This core network encompasses key PD-associated pathways, including ubiquitination, mitochondrial function, protein processing, RNA metabolism, and vesicular transport. Proteomics analysis showed a consistent alteration in proteins of dopamine metabolism, indicating a defect of dopaminergic metabolism in PINK1-ILE368ASN neurons. Our findings suggest the existence of a network onto which pathways associated with PD pathology converge, and offers an inclusive interpretation of the phenotypic heterogeneity of PD.

Single-cell gene expression analysis in keloid skin-derived fibroblasts subjected to repeated stretch stimulation

Single-cell gene expression analysis in keloid skin-derived fibroblasts subjected to repeated stretch stimulation

Recent Advances in the Understanding of Specific Efferent Pathways Emerging From the Cerebellum.

The cerebellum has a long history in terms of research on its network structures and motor functions, yet our understanding of them has further advanced in recent years owing to technical developments, such as viral tracers, optogenetic and chemogenetic manipulation, and single cell gene expression analyses. Specifically, it is now widely accepted that the cerebellum is also involved in non-motor functions, such as cognitive and psychological functions, mainly from studies that have clarified neuronal pathways from the cerebellum to other brain regions that are relevant to these functions. The techniques to manipulate specific neuronal pathways were effectively utilized to demonstrate the involvement of the cerebellum and its pathways in specific brain functions, without altering motor activity. In particular, the cerebellar efferent pathways that have recently gained attention are not only monosynaptic connections to other brain regions, including the periaqueductal gray and ventral tegmental area, but also polysynaptic connections to other brain regions, including the non-primary motor cortex and hippocampus. Besides these efferent pathways associated with non-motor functions, recent studies using sophisticated experimental techniques further characterized the historically studied efferent pathways that are primarily associated with motor functions. Nevertheless, to our knowledge, there are no articles that comprehensively describe various cerebellar efferent pathways, although there are many interesting review articles focusing on specific functions or pathways. Here, we summarize the recent findings on neuronal networks projecting from the cerebellum to several brain regions. We also introduce various techniques that have enabled us to advance our understanding of the cerebellar efferent pathways, and further discuss possible directions for future research regarding these efferent pathways and their functions.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Flow-FISH as a Tool for Studying Bacteria, Fungi and Viruses.

Many techniques are currently in use to study microbes. These can be aimed at detecting, identifying, and characterizing bacterial, fungal, and viral species. One technique that is suitable for high-throughput analysis is flow cytometry-based fluorescence in situ hybridization, or Flow-FISH. This technique employs (fluorescently labeled) probes directed against DNA or (m)RNA, for instance targeting a gene or microorganism of interest and provides information on a single-cell level. Furthermore, by combining Flow-FISH with antibody-based protein detection, proteins of interest can be measured simultaneously with genetic material. Additionally, depending on the type of Flow-FISH assay, Flow-FISH can also be multiplexed, allowing for the simultaneous measurement of multiple gene targets and/or microorganisms. Together, this allows for, e.g., single-cell gene expression analysis or identification of (sub)strains in mixed cultures. Flow-FISH has been used in mammalian cells but has also been extensively employed to study diverse microbial species. Here, the use of Flow-FISH for studying microorganisms is reviewed. Specifically, the detection of (intracellular) pathogens, studying microorganism biology and disease pathogenesis, and identification of bacterial, fungal, and viral strains in mixed cultures is discussed, with a particular focus on the viruses EBV, HIV-1, and SARS-CoV-2.

PRC1 drives Polycomb-mediated gene repression by controlling transcription initiation and burst frequency.

The Polycomb repressive system plays a fundamental role in controlling gene expression during mammalian development. To achieve this, Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) bind target genes and use histone modification-dependent feedback mechanisms to form Polycomb chromatin domains and repress transcription. The inter-relatedness of PRC1 and PRC2 activity at these sites has made it difficult to discover the specific components of Polycomb chromatin domains that drive gene repression and to understand mechanistically how this is achieved. Here, by exploiting rapid degron-based approaches and time-resolved genomics, we kinetically dissect Polycomb-mediated repression and discover that PRC1 functions independently of PRC2 to counteract RNA polymerase II binding and transcription initiation. Using single-cell gene expression analysis, we reveal that PRC1 acts uniformly within the cell population and that repression is achieved by controlling transcriptional burst frequency. These important new discoveries provide a mechanistic and conceptual framework for Polycomb-dependent transcriptional control.

Dual checkpoint blockade of CD47 and PD-L1 using an affinity-tuned bispecific antibody maximizes antitumor immunity

BackgroundT cell checkpoint immunotherapies have shown promising results in the clinic, but most patients remain non-responsive. CD47-signal regulatory protein alpha (SIRPα) myeloid checkpoint blockade has shown early clinical activity in...

Developmental genetics of color pattern establishment in cats

Intricate color patterns are a defining aspect of morphological diversity in the Felidae. We applied morphological and single-cell gene expression analysis to fetal skin of domestic cats to identify when, where, and how, during fetal development, felid color patterns are established. Early in development, we identify stripe-like alterations in epidermal thickness preceded by a gene expression pre-pattern. The secreted Wnt inhibitor encoded by Dickkopf 4 plays a central role in this process, and is mutated in cats with the Ticked pattern type. Our results bring molecular understanding to how the leopard got its spots, suggest that similar mechanisms underlie periodic color pattern and periodic hair follicle spacing, and identify targets for diverse pattern variation in other mammals.

Combining single cell gene expression analysis and a 3D in vitro liver model to investigate cell type-specific responses to pro-fibrotic TGF-ß1.

Combining single cell gene expression analysis and a 3D in vitro liver model to investigate cell type-specific responses to pro-fibrotic TGF-ß1.

Lgr6 marks epidermal stem cells with a nerve-dependent role in wound re-epithelialization.

Stem cells support lifelong maintenance of adult organs, but their specific roles during injury are poorly understood. Here we demonstrate that Lgr6 marks a regionally restricted population of epidermal stem cells that interact with nerves and specialize in wound re-epithelialization. Diphtheria toxin-mediated ablation of Lgr6 stem cells delays wound healing, and skin denervation phenocopies this effect. Using intravital imaging to capture stem cell dynamics after injury, we show that wound re-epithelialization by Lgr6 stem cells is diminished following loss of nerves. This induces recruitment of other stem cell populations, including hair follicle stem cells, which partially compensate to mediate wound closure. Single-cell lineage tracing and gene expression analysis reveal that the fate of Lgr6 stem cells is shifted toward differentiation following loss of their niche. We conclude that Lgr6 epidermal stem cells are primed for injury response and interact with nerves to regulate their fate.

A human forebrain organoid model of fragile X syndrome exhibits altered neurogenesis and highlights new treatment strategies.

Fragile X syndrome (FXS) is caused by the loss of fragile X mental retardation protein (FMRP), an RNA-binding protein that can regulate the translation of specific mRNAs. Here we have developed an FXS human forebrain organoid model and observed the loss of FMRP led to dysregulated neurogenesis, neuronal maturation, and neuronal excitability. Bulk and single-cell gene expression analyses of FXS forebrain organoids revealed that the loss of FMRP altered gene expression in a cell type-specific manner. The developmental deficits in FXS forebrain organoids could be rescued by inhibiting the phosphoinositide 3-kinase pathway, but not the metabotropic glutamate pathway disrupted in the FXS mouse model. We identified a large number of human-specific mRNAs bound by FMRP. One of these human-specific FMRP targets, CHD2, contributed to the altered gene expression in FXS organoids. Collectively, our study revealed molecular, cellular, and electrophysiological abnormalities associated with the loss of FMRP during human brain development.

CoCoA-diff: counterfactual inference for single-cell gene expression analysis

Finding a causal gene is a fundamental problem in genomic medicine. We present a causal inference framework, CoCoA-diff, that prioritizes disease genes by adjusting confounders without prior knowledge of control variables in single-cell RNA-seq data. We demonstrate that our method substantially improves statistical power in simulations and real-world data analysis of 70k brain cells collected for dissecting Alzheimer’s disease. We identify 215 differentially regulated causal genes in various cell types, including highly relevant genes with a proper cell type context. Genes found in different types enrich distinctive pathways, implicating the importance of cell types in understanding multifaceted disease mechanisms.

Abstract 131: Remodeling of stromal-epithelial interactions in breast cancer progression as inferred from regional and single-cell gene expression analysis

Abstract Background: The transition from ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is defined by the invasion of malignant cells into surrounding tissues, but associated changes in the cellular composition and interactions in the tumor microenvironment are poorly understood. Systematic analysis of gene expression differences between DCIS and IDC are confounded by cellular heterogeneity and few studies have separated stromal and epithelial contributions or distinguished pure DCIS from those synchronous with IDC. Methods: The gene expression of 192 microdissected regions from 140 cases was assembled from 5 studies and included DCIS (83 epithelial, 22 stromal) and IDC (67 epithelial, 20 stromal) cases. Differences in infiltration of 10 immune cell types were measured using expression signatures. Cell-cell interactions between stromal and epithelial regions were measured using the co-expression of 1175 curated pairs of ligand-receptor genes and compared between DCIS and IDC using a permutation test. The corresponding interacting cell types were identified using RNA sequencing of 5444 single nuclei extracted from fine needle aspirates of two excised DCIS specimens. Results: Stromal regions of Her2+ cases had the highest level of immune infiltration compared to IDC and Her2- cases. Of the measured immune cell types, B cells showed a progressive depletion from normal to DCIS to IDC. The expression-based measurement of stromal-epithelial interactions identified 99 and 115 ligand-receptor interactions stronger in DCIS and IDC, respectively (FDR<0.1). To precisely identify the corresponding interacting cell types, the expression profile of cells within the DCIS microenvironment was determined using single-nucleus RNA sequencing from two DCIS patients. The analysis identified 9 cell types, including luminal, basal, macrophage, adipocyte and endothelial, and 54% of the candidate cell-cell interactions could be mapped to at least one cell type pair. Based on these cell type mappings, interactions between luminal cells and fibroblasts were gained in IDC while those involving the vascular endothelium were lost, including interactions between CD24 and SELP, an interaction involved in leukocyte recruitment. Increased macrophage autocrine interactions were identified in both IDC stroma and epithelium through PLAU-PLAUR co-expression, an interaction previously associated with a transition to the M2 phenotype. Conclusion: The analysis of published gene expression studies with novel computational methods, augmented with a single-cell landscape representative of the disease, can help compensate for the difficulty in studying pure DCIS specimens. The microenvironment changes in cellular dynamics involved both immune and non-immune cell types suggesting mechanisms other than direct immune escape contribute to progression. Citation Format: Adam Officer, Andre Dempsey, Farnaz Hasteh, Michal Slyper, Asa Segerstolpe, Joanna Klughammer, Judit Jane-Valbuena, Orit Rozenblatt-Rosen, Aviv Regev, Christina Yau, Olivier Harismendy. Remodeling of stromal-epithelial interactions in breast cancer progression as inferred from regional and single-cell gene expression analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 131.

Single-Cell Transcriptional Profiling of Acute Myeloid Leukemia Identifies Prognostic Transcriptional Profiles at Diagnosis

Acute myeloid leukemia (AML) is a lethal cancer with a survival of less than 50%. Standard cytotoxic therapies frequently induce complete remission, but patients frequently relapse and die of their disease. Leukemia stem cells (LSCs) are the leukemia cells with self-renewal potential and ability to recapitulate the disease. In hematopoietic stem cells, the mechanisms of proliferation are distinct from self-renewal (Li et al. Nature 2013). Consequently, targeting proliferation may explain the failure of traditional chemotherapy to target LSCs and eradicate AML. Our goal is define the molecular mechanisms that allow AML to relapse. We previously showed that activated NRAS (NRASG12V) facilitates self-renewal in the LSC-enriched subpopulation of a mouse model of AML (Mll-AF9 / NRASG12V, Sachs et al. Blood 2014, Kim et al. Blood 2009). We hypothesize that understanding the NRAS-activated pathways required for self-renewal at the single-cell level can be used to identify clinically relevant features of human AML.We used single-cell RNA sequencing to identify the self-renewing cells among the LSC-enriched subgroup in this model (Mac1LowKit+Sca1+, “MKS”). We identified three discrete transcriptional profiles among the LSC-enriched subpopulation and found that that two of these profiles (Profile 1 and Profile 2) are NRASG12V-dependent. These two profiles can be differentiated by CD36 and CD69 expression. We sorted the MKS LSCs based on CD36 and CD69 expression. Sorted LSC subsets were transplanted into recipient mice to compare their ability to transfer leukemia as a measure of their self-renewal capacity. We found that MKS-CD36-CD69+ cells (consistent with Profile 1) rapidly transferred leukemia with high penetrance in 20 of 22 mice. In contrast, MKS-CD36+CD69- cells (Profile 2) failed transfer leukemia in most mice; only 2 of 25 of these mice developed AML (p < 0.004).In our previous work, we demonstrated that the NRASG12V-activated self-renewal gene expression profile that we identified in our murine model was expressed in human AML, suggesting that the gene expression behavior of LSCs from this model may recapitulate the gene expression behavior of human LSCs (Sachs et al. Blood 2014). We used our murine single-cell self-renewal transcriptional profile to define a 96-gene panel consisting of 88 genes from this profile and 8 housekeeping genes. We sorted primary, diagnostic human AML cells for leukemia stem and progenitor cells (CD34+CD38-) and performed single-cell qPCR on these cells using our 96-gene panel. We found that a subset of these human LSCs preferentially expresses Profile 1, the self-renewal gene expression profile that we identified in our murine model, and another subset preferentially expresses Profile 2 (the profile associated with no leukemia-reconstituting capacity).Next, we investigated whether CD36 and CD69 delineate clinically relevant transcriptional subgroups in human AML. We used previously published, clinically annotated gene expression datasets of human AML that include associated survival outcomes data (TCGA, GSE12417 (Metzeler et al. Blood 2008), and GSE6891 (Verhaak et al. Haematologica 2009)). Gene Cluster Expression Summary Score (GCESS, Scott et al. under review) analysis identified gene clusters associated with CD69 and CD36 in our murine single-cell dataset. We found that the CD36 and CD69-associated clusters are conserved in the human transcriptional datasets. More importantly, patients that express either the CD69 or the CD36-associated gene expression profile suffer from shorter survival times in these three, independent human transcriptional datasets.In these experiments, we use a murine model of AML to define the LSC self-renewal gene expression profile at the single-cell level and functionally validate this profile in vivo . Analogous to the murine model, a subset of human AML stem and progenitor cells expresses this LSC self-renewal gene expression profile at the single-cell level. Finally, we found that the gene expression profiles we identified in our murine single-cell data are associated with poor outcomes in human AML indicating that our single-cell gene expression analyses can be used to identify prognostic patient subgroups. These findings suggest that these gene expression profiles can be used to identify functional transcriptional modules associated with poor outcome in human AML. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Germinal Center Program De-Synchronization and Intra-Patient Heterogeneity in Follicular Lymphoma B-Cells Revealed By Integrative Single-Cell Analysis

IntroductionFollicular lymphoma (FL) is an indolent B-cell neoplasm with histological, phenotypical and transcriptional features of germinal center (GC) B-cells, suggesting that FL B-cells are “frozen” in a GC B-cell state. In normal GC B-cells, affinity maturation relies on iterative cycles of somatic hypermutation and proliferation followed by affinity-based selection. Thus GC B-cells are functionally heterogeneous. Although FL sub-clonal genetic diversity is well documented, tumor cell functional heterogeneity in FL has not been studied in detail. Bulk-level transcriptomic analyses fail to capture cellular heterogeneity and are thus insufficient to compare FL B-cells to the heterogeneous GC B-cell subset. Here, we used integrative single-cell analysis of phenotype, gene expression and IGH sequence to assess and compare functional heterogeneity in FL B-cells and GC B-cells.MethodsNormal B-cells were sorted from spleens or tonsil of healthy human donors (n=4), and FL B-cells from diagnostic lymph node biopsies (n=5), all available as frozen viable cell suspensions. Single normal or malignant B-cells were sorted by flow cytometry for parallel analysis of phenotype (9-color panel), gene expression (91-gene panel by microfluidic RT-qPCR), and IGH sequence (RT-PCR followed by Sanger sequencing; FL B-cells only). Custom and existing bioinformatics analysis pipelines were combined for quality control and cell filtering, dimensionality reduction (PCA, t-SNE), clustering, pseudo-time analysis, IGH sequence analysis and integrative data analysis.ResultsIn normal B-cells, single-cell gene expression profiling segregated major cell types (antibody-producing cells, memory B-cells and GC B-cells) and enabled the reconstruction of differentiation trajectories within cell types. Specifically, our data revealed a cyclic continuum of transitional GC B-cell states where synchronized expression of gene modules characterized human GC B-cell functional heterogeneity. Single-cell gene expression profiles defined FL B-cells as an independent cell type, distinct from GC B-cells. In particular, the expression of most characteristic GC-associated genes was de-synchronized in FL B-cells from all patients. Clustering of single FL B-cell 91-gene expression profiles revealed inter-patient and intra-patient heterogeneity, and defined common FL B-cell subsets found in most patients. Intra-patient heterogeneity was not linked to sub-clonal divergence as defined by IGH sequencing.ConclusionsUsing integrative single-cell analyses, we have shown that synchronized expression of characteristic gene modules drives GC B-cells through a cyclic continuum of transitional states. De-synchronization of that program in FL B-cells, and substantial intra-patient gene expression heterogeneity, suggest that FL B-cells are functionally plastic and not blocked to a GC B-cell state. DisclosuresNo relevant conflicts of interest to declare.

Single-Cell Gene Expression Analysis Reveals Gene Regulatory Networks Driving Proliferation in Pituitary Stem and Endocrine Cells

A fundamental question for pituitary development and disease is to understand the mechanisms that regulate proliferation, quiescence, and differentiation of stem cells and endocrine lineage-committed precursor cells. Pituitary stem cells, marked by expression of SOX2, are highly proliferative during development and the early postnatal period. This pool becomes quiescent over time, but stem cells retain the ability to re-enter the cell cycle and differentiate into nascent endocrine cells of all lineages in response to physiological demands. The rodent pituitary gland undergoes substantial growth in the postnatal period, and much of this increase in size is due to proliferation of committed progenitors, such as Pou1f1-expressing cells. We performed single-cell RNA transcriptomics analyses of over 8,000 male and female 4-day-old mouse pituitary cells in order to assess stem cell heterogeneity and to identify novel pituitary stem cell biomarkers. We identified a number of factors enriched in pituitary stem cells relative to differentiating cells, including the transcription factors TGIF1 and NR4A3 as well as several members of the nuclear factor I family (NFIA, NFIB, NFIX). We also detected stem cell-specific expression of the cortisol synthesizing enzyme HSD11B1 and folate receptor FOLR1, suggesting novel roles for these pathways in pituitary stem cells. There were few transcriptomic differences between proliferating and non-proliferating stem cells. However, proliferating stem cells and proliferating committed progenitors shared expression of cell-cycle associated genes and novel transcription factors such as BRCA1 and E2F1. Furthermore, single-cell gene network inference and clustering (SCENIC) analyses demonstrated activation of common gene regulatory networks in both proliferating stem and proliferating endocrine populations, including both the E2f1 and Brca1 regulons. RNA velocity, trajectory, and phylogenetic analyses and find that proliferating stem and endocrine cells are likely independent cellular states. Instead, they support the idea that proliferating stem cells become quiescent and transition to committed progenitors that re-enter the cell cycle and then subsequently activate hormone gene expression. In conclusion, our single-cell gene expression analyses of early postnatal pituitary cells have shed light on the developmental trajectory from proliferating stem cell to quiescent differentiated hormone-producing cells.

Imaging cell lineage with a synthetic digital recording system.

During multicellular development, spatial position and lineage history play powerful roles in controlling cell fate decisions. Using a serine integrase-based recording system, we engineered cells to record lineage information in a format that can be read out in situ. The system, termed integrase-editable memory by engineered mutagenesis with optical in situ readout (intMEMOIR), allowed in situ reconstruction of lineage relationships in cultured mouse cells and flies. intMEMOIR uses an array of independent three-state genetic memory elements that can recombine stochastically and irreversibly, allowing up to 59,049 distinct digital states. It reconstructed lineage trees in stem cells and enabled simultaneous analysis of single-cell clonal history, spatial position, and gene expression in Drosophila brain sections. These results establish a foundation for microscopy-readable lineage recording and analysis in diverse systems.

Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection

Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.