Biology subjects

Morris, S. A.

Publications and source records attributed to Morris, S. A..

15 recordsLinked to original sources

bioRxiv2026

Clonal memory in human embryonic stem cells biases fate potential during endoderm differentiation

Cell fate decisions during development are shaped not only by extrinsic signals but also by heritable intrinsic states passed on across cell division. The extent to which this phenomenon, termed clonal memory, can explain the persistent heterogeneity observed from directed differentiation of human embryonic stem cells is unclear. Here, we combine lineage tracing with single-cell transcriptomics and chromatin accessibility profiling to track clonal behaviour across human embryonic stem cell differentiation towards definitive endoderm. Using a lentiviral barcoding system coupled with a split-well sampling strategy, we find that clonally related cells exhibit reproducible, probabilistic fate outcomes that cannot be explained by signalling environment alone. Fate-biased clones are transcriptionally indistinguishable at the pluripotent stage yet display distinct chromatin accessibility landscapes at lineage-specific cis-regulatory elements. Pre-existing accessibility at these lineage-specific regulatory regions distinguish clones that undergo successful endoderm differentiation from those that generate off-target mesoderm derivatives. Together, these findings provide an explanation for how off-target populations arise during directed differentiation, identifying heritable chromatin states within pluripotent cultures as a source of variability relevant to stem cell-derived in vitro models and cell therapies.

developmental biology↗

bioRxiv2025

Analysis of menstrual effluent uncovers endometriosis-specific cell populations and impaired cellular pathway processes

Endometriosis is a chronic gynecological disease affecting 1 in 10 reproductive-aged women and is characterized by the ectopic presence of endometrial tissue outside the uterus. The leading hypothesis for disease etiology is via the reflux of menstrual effluent (ME) into the peritoneal cavity. ME is a complex mixture of viable endometrial tissue, proteins, and immune cells which serve specialized functions during menstruation to support and repair the endometrium. We hypothesized shifts in the subpopulations of immune cells present during retrograde menstruation may alter the microenvironment of the peritoneal cavity leading to a favorable environment for uterine tissue attachment and survival of endometriotic lesions. Menstrual effluent collected on days 1 and 2 of menstruation identified from women with endometriosis distinct morphological features, increased subpopulations of aged neutrophils, increased anti-inflammatory macrophages, and overall impaired clearance pathways that hijack endometrial clearance likely contributing to the development and progression of endometriosis.

immunology↗

bioRxiv2023

Single-cell lineage tracing reveals hierarchy and mechanism of adipocyte precursor maturation

White adipose tissue is crucial in various physiological processes. In response to high caloric intake, adipose tissue may expand by generating new adipocytes. Adipocyte precursor cells (progenitors and preadipocytes) are essential for generating mature adipocytes, and single-cell RNA sequencing provides new means to identify these populations. Here, we characterized adipocyte precursor populations in the skin, an adipose depot with rapid and robust generation of mature adipocytes. We identified a new population of immature preadipocytes, revealed a biased differentiation potential of progenitor cells, and identified Sox9 as a critical factor in driving progenitors toward adipose commitment, the first known mechanism of progenitor differentiation. These findings shed light on the specific dynamics and molecular mechanisms underlying rapid adipogenesis in the skin.

developmental biology↗

bioRxiv2022

Multiomic single-cell lineage tracing to dissect fate-specific gene regulatory programs

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics enabling high-resolution analysis of cell states while preserving lineage relationships. However, reliance on transcriptional profiling limits their adaptation to an ever-expanding tool kit of multiomic single-cell assays. With CellTag-multi, we present a novel approach for independently profiling lineage barcodes with single-cell chromatin accessibility without relying on co-assay of transcriptional state, paving the way for truly multiomic lineage tracing. We validate CellTag-multi in mouse hematopoiesis, characterizing transcriptional and epigenomic lineage priming across progenitor cell populations. In direct reprogramming of fibroblasts to endoderm progenitors, we use CellTag-multi to comprehensively link early cell state with reprogramming outcomes, identifying core regulatory programs underlying on-target and off-target reprogramming. Further, we reveal the Transcription Factor (TF) Zfp281 as a novel regulator of reprogramming outcome, biasing cells towards an off-target mesenchymal fate via its regulation of TGF-{beta} signaling. Together, these results establish CellTag-multi as a novel lineage tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

genomics↗

bioRxiv2022

Defining Cardiac Recovery at Single Cell Resolution

Recovery of cardiac function is the ultimate goal of heart failure therapy. Unfortunately, cardiac recovery remains a rare and poorly understood phemomenon. Herein, we performed single nucleus RNA-sequencing (snRNA-seq) from non-diseased donors and heart failure patients. By comparing patients who recovered LV systolic function following LV assist device implantation to those who did not recover and donors, we defined the cellular and transcriptional landscape and predictors of cardiac recovery. We sequenced 40 hearts and recovered 185,881 nuclei with 13 distinct cell types. Using pseudobulk differential expression analysis to explicate cell specific signatures of cardiac recovery, we observed that recovered cardiomyocytes do not revert to a normal state, and instead, retain transcriptional signatures observed in heart failure. Macrophages and fibroblasts displayed the strongest signatures of recovery. While some evidence of reversion to a normal state was observed, many heart failure associated genes remained elevated and recovery signatures were predominately indicative of a biological state that was unique from donor and heart failure conditions. Acquisition of recovery states was associated with improved LV systolic function. Pro-inflammatory macrophages and inflammatory signaling in fibroblasts were identified as negative predictors of recovery. We identified downregulation of RUNX1 transcriptional activity in macrophages and fibroblasts as a central event associated with and predictive of cardiac recovery. In silico perturbation of RUNX1 in macrophages and fibroblasts recapitulated the transcriptional state of cardiac recovery. This prediction was corroborated in a mouse model of cardiac recovery mediated by BRD4 inhibition where we observed a decrease in macrophage and fibroblast Runx1 expression, diminished chromatin accessibility within peaks linked to the Runx1 locus, and acquisition of recovery signatures. These findings suggest that cardiac recovery is a unique biological state and identify RUNX1 as a possible therapeutic target to facilitate cardiac recovery.

genomics↗

bioRxiv2022

Gene Regulatory Network Reconfiguration in Direct Lineage Reprogramming

In direct lineage reprogramming, transcription factor (TF) overexpression reconfigures Gene Regulatory Networks (GRNs) to convert cell identities between fully differentiated cell types. We previously developed CellOracle, a computational pipeline that integrates single-cell transcriptome and epigenome profiles to infer GRNs. CellOracle leverages these inferred GRNs to simulate gene expression changes in response to TF perturbation, enabling network re-configuration during reprogramming to be interrogated in silico. Here, we integrate CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct lineage reprogramming paradigm. By linking early network state to reprogramming success or failure, we reveal distinct network configurations underlying different reprogramming outcomes. Using these network analyses and in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting cell identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations at high resolution, providing new mechanistic insights into the regulation and reprogramming of cell identity.

genomics↗

bioRxiv2022

A cancer stem cell population underlies a multi-lineage phenotype and drug resistance in prostate cancer

PurposeTo resist lineage-dependent therapies such as androgen receptor inhibition in prostate cancer, cancer cells often adopt a stem-like state resulting in lineage-plasticity and phenotypic heterogeneity. We assessed the dynamics of lineage determination and cellular subpopulation expansion in treatment-resistant adenocarcinoma, amphicrine, and small cell neuroendocrine castrate resistant prostate cancers (CRPCs). Experimental DesignWe developed CRPC patient-derived organoid models that preserve heterogeneity of the originating tumor, including an amphicrine model harboring epigenetic driver mutations, ARID1A and ARID1B, and displaying a range of luminal and neuroendocrine phenotypes. We used single-cell RNA-seq, barcode lineage-tracing, single-cell ATAC-seq, and RNA-FISH to delineate the subpopulation structure of the heterogeneous organoids and define the lineage hierarchy, determine potential transcriptional regulators of amphicrine lineage-plasticity, and identify subpopulation-specific molecular targets for therapeutic intervention. ResultsTranscriptionally similar stem/progenitor cells were identified for all lineage populations. Lineage tracing in amphicrine CRPC showed that heterogeneity originated from distinct subclones of infrequent stem/progenitor cells that produced mainly quiescent differentiated amphicrine progeny. Amphicrine cells were enriched for secretory luminal, mesenchymal, and enzalutamide treatment persistent signatures. By contrast, adenocarcinoma CRPC had a less defined hierarchy, as progeny originated from stem/progenitor cells and self-renewing differentiated luminal cells. NEPC was composed almost exclusively of self-renewing stem/progenitor cells. Amphicrine stem cells demonstrated concurrent transcription factor activities associated with stem/progenitor, luminal epithelial and mesenchymal lineages. Finally, the amphicrine stem/progenitor subpopulation was specifically depleted with an AURKA inhibitor, which blocked tumor growth. ConclusionsThese data illuminate distinct origins and dynamics of subtype-specific CRPC plasticity in addition to demonstrating a strategy for targeting differentiation-competent stem cells. Translational RelevanceFor advanced prostate cancer, therapeutic resistance to androgen signaling suppression increasingly involves the development of lineage plasticity. The cellular states of transition and subpopulation heterogeneity that underlie lineage-plasticity are not well understood, which is an ongoing challenge to the design of effective treatments. Using patient-derived organoid models of various CRPC lineage subtypes, we observed distinct patterns with respect to stem/progenitor activity and associated growth phenotypes. The simultaneous expression of AR-driven and neuroendocrine identities, so-called amphicrine tumors, are thought to be an early dedifferentiation stage in plasticity-mediated resistance. We observed in an epigenetically-driven, amphicrine model of CRPC that a rare but necessary bipotent stem/progenitor population is suppressed by AURKA inhibitors, leading to tumor regression, while ARPC demonstrates both self-renewing differentiated luminal cells and stem/progenitors. These data suggest that AURKA inhibition may block the amplification of a resistance dedifferentiation pathway and should be considered in combination with AR signaling inhibitors for ARPC with characteristics of lineage plasticity.

cancer biology↗

bioRxiv2020

Capybara: A computational tool to measure cell identity and fate transitions

Transitions in cell identity are fundamental to development, reprogramming, and disease. Single-cell technologies enable the dissection of tissue composition on a cell-by-cell basis in complex biological systems. However, highly-sparse single-cell RNA-seq data poses challenges for cell-type identification algorithms based on bulk RNA-seq. Single-cell analytical tools are also limited, where they require prior biological knowledge and typically classify cells in a discrete, categorical manner. Here, we present a computational tool, Capybara, designed to measure cell identity as a continuum, at single-cell resolution. This approach enables the classification of discrete cell entities but also identifies cells harboring multiple identities, supporting a metric to quantify cell fate transition dynamics. We benchmark the performance of Capybara against other existing classifiers and demonstrate its efficacy to annotate cells and identify critical transitions within a well-characterized differentiation hierarchy, hematopoiesis. Our application of Capybara to a range of reprogramming strategies reveals previously uncharacterized regional patterning and identifies a putative in vivo correlate for an engineered cell type that has, to date, remained undefined. These findings prioritize interventions to increase the efficiency and fidelity of cell engineering strategies, showcasing the utility of Capybara to dissect cell identity and fate transitions. Capybara code and documentation are available at https://github.com/morris-lab/Capybara.

genomics↗

bioRxiv2020

CellOracle: Dissecting cell identity via network inference and in silico gene perturbation

Here, we present CellOracle, a computational tool that integrates single-cell transcriptome and epigenome profiles to infer gene regulatory networks (GRNs), critical regulators of cell identity. Leveraging inferred GRNs, we simulate gene expression changes in response to transcription factor (TF) perturbation, enabling network configurations to be interrogated in silico, facilitating their interpretation. We validate the efficacy of CellOracle to recapitulate known regulatory changes across hematopoiesis, correctly predicting the outcomes of well-characterized TF perturbations. Integrating CellOracle analysis with lineage tracing of direct reprogramming reveals distinct network configurations underlying different reprogramming failure modes. Furthermore, analysis of GRN reconfiguration along successful reprogramming trajectories identifies new factors to enhance target cell yield, uncovering a role for the AP-1 subunit Fos, with the hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations, at high-resolution, promoting new mechanistic insights into the regulation and reprogramming of cell identity.

genomics↗

bioRxiv2020

High-resolution transcriptional and morphogenetic profiling of cells from micropatterned human embryonic stem cell gastruloid cultures

During mammalian gastrulation, germ layers arise and are shaped into the body plan while extraembryonic layers sustain the embryo. Human embryonic stem cells, cultured with BMP4 on extracellular matrix micro-discs, reproducibly differentiate into gastruloids, expressing markers of germ layers and extraembryonic cells with radial organization. Single-cell RNA sequencing and cross-species comparisons with mouse and cynomolgus monkey gastrulae suggest that gastruloids contain seven major cell types, including epiblast, ectoderm, mesoderm, endoderm, and extraembryonic trophoblast- and amnion-like cells. Upon gastruloid dissociation, single cells reseeded onto micro-discs were motile and aggregated with the same but segregated from distinct cell types. Ectodermal cells segregated from endodermal and extraembryonic cells but mixed with mesodermal cells. Our work demonstrates that the gastruloid system supports primate-specific features of embryogenesis, and that gastruloid cells exhibit evolutionarily conserved sorting behaviors. This work generates a resource for transcriptomes of human extraembryonic and embryonic germ layers differentiated in a stereotyped arrangement.

developmental biology↗

bioRxiv2019

Single-Cell Analysis Reveals Regional Reprogramming during Adaptation to Massive Small Bowel Resection in Mice

Background & AimsThe small intestine (SI) displays regionality in nutrient and immunological function. Following SI tissue loss (as occurs in short gut syndrome, or SGS), remaining SI must compensate, or adapt; the capacity of SI epithelium to reprogram its regional identity has not been described. Here, we apply single-cell resolution analyses to characterize molecular changes underpinning adaptation to SGS.\n\nMethodsSingle-cell RNA-sequencing was performed on epithelial cells isolated from distal SI of mice following 50% proximal small bowel resection (SBR) vs. sham surgery. Single-cell profiles were clustered based on transcriptional similarity, reconstructing differentiation events from intestinal stem cells (ISCs) through to mature enterocytes. An unsupervised computational approach to score cell identity was used to quantify changes in regional (proximal vs distal) SI identity, validated using immunofluorescence, immunohistochemistry, qPCR, western blotting, and RNA-FISH.\n\nResultsUniform Manifold Approximation and Projection-based clustering and visualization revealed differentiation trajectories from ISCs to mature enterocytes in sham and SBR. Cell identity scoring demonstrated segregation of enterocytes by regional SI identity: SBR enterocytes assumed more mature proximal identities. This was associated with significant upregulation of lipid metabolism and oxidative stress gene expression, which was validated via orthogonal analyses. Observed upstream transcriptional changes suggest retinoid metabolism and proximal transcription factor Creb3l3 drive proximalization of cell identity in response to SBR.\n\nConclusionsAdaptation to proximal SBR involves regional reprogramming of ileal enterocytes toward a proximal identity. Interventions bolstering the endogenous reprogramming capacity of SI enterocytes--conceivably by engaging the retinoid metabolism pathway--merit further investigation, as they may increase enteral feeding tolerance, and obviate intestinal failure, in SGS.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=83 SRC=\"FIGDIR/small/615054v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@156d1b4org.highwire.dtl.DTLVardef@e1a3fcorg.highwire.dtl.DTLVardef@1d519bforg.highwire.dtl.DTLVardef@18d4e9d_HPS_FORMAT_FIGEXP M_FIG C_FIG SynopsisHere, single-cell RNA sequencing reveals interactions between the retinoid metabolism pathway and regional reprogramming of distal small intestinal epithelium to a proximal identity following proximal small bowel resection. This provides novel insight into physiological adaptation to short gut syndrome.

genomics↗

bioRxiv2019

Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

In situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

genomics↗

bioRxiv2018

CellTag Indexing: a genetic barcode-based multiplexing tool for single-cell technologies

Single-cell technologies have seen rapid advancements in recent years, along with new analytical challenges and opportunities. These high-throughput assays increasingly require special consideration in experimental design, sample multiplexing, batch effect removal, and data interpretation. Here, we describe a lentiviral barcode-based multiplexing approach, CellTag Indexing, where we transduce and label samples that can then be pooled together for downstream application and analysis. By introducing predefined genetic barcodes that are transcribed and readily detected, we can reliably read out sample identity via genomic or transcriptomic profiling, permitting the simultaneous assessment of cell grouping and transcriptional state. We validate and demonstrate the utility of CellTag Indexing by sequencing transcriptomes at single-cell resolution using a variety of cell types including mouse pre-B cells, primary mouse embryonic fibroblasts, human HEK293T cells, and mouse induced endoderm progenitors. Furthermore, we establish CellTag Indexing as a valuable tool for multiplexing direct lineage reprogramming perturbation experiments. We present CellTag Indexing as a broadly applicable genetic multiplexing tool that is complementary with existing single-cell RNA-sequencing and multiplexing strategies.

genomics↗

bioRxiv2017

Comparative analysis of kidney organoid and adult human kidney single cell and single nucleus transcriptomes

Kidney organoids differentiated from human pluripotent stem cells hold great promise for understanding organogenesis, modeling disease and ultimately as a source of replacement tissue. Realizing the full potential of this technology will require better differentiation strategies based upon knowledge of the cellular diversity and differentiation state of all cells within these organoids. Here we analyze single cell gene expression in 45,227 cells isolated from 23 organoids differentiated using two different protocols. Both generate kidney organoids that contain a diverse range of kidney cells at differing ratios as well as non-renal cell types. We quantified the differentiation state of major organoid kidney cell types by comparing them against a 4,259 single nucleus RNA-seq dataset generated from adult human kidney, revealing immaturity of all kidney organoid cell types. We reconstructed lineage relationships during organoid differentiation through pseudotemporal ordering, and identified transcription factor networks associated with fate decisions. These results define impressive kidney organoid cell diversity, identify incomplete differentiation as a major roadblock for current directed differentiation protocols and provide a human adult kidney snRNA-seq dataset against which to benchmark future progress.

developmental biology↗

bioRxiv2017

Single-cell analysis of clonal dynamics in direct lineage reprogramming: a combinatorial indexing method for lineage tracing

Single-cell technologies are offering unprecedented insight into complex biology, revealing the behavior of rare cell populations that are typically masked in bulk population analyses. The application of these methodologies to cell fate reprogramming holds particular promise as the manipulation of cell identity is typically inefficient, generating heterogeneous cell populations. One current limitation of single-cell approaches is that lineage relationships are lost as a result of cell processing, restricting interpretations of the data collected. Here, we present a single-cell resolution lineage-tracing approach based on the combinatorial indexing of cells, CellTagging. Application of this method, in concert with high-throughput single-cell RNA-sequencing, reveals the transcriptional dynamics of direct reprogramming from fibroblasts to induced endoderm progenitors. These analyses demonstrate that while many cells initiate reprogramming, complete silencing of fibroblast identity and transition to a progenitor-like state represents a rare event. Clonal analyses uncover a remarkable degree of heterogeneity arising from individual cells. Overall, very few cells fully reprogram to generate expanded populations with a low degree of clonal diversity. Extended culture of these engineered cells reveals an instability of the reprogrammed state and reversion to a fibroblast-like phenotype. Together, these results demonstrate the utility of our lineage-tracing approach to reveal dynamics of lineage reprogramming, and will be of broad utility in many cell biological applications.

genomics↗