Biology subjects

Kamimoto, K.

Publications and source records attributed to Kamimoto, K..

7 recordsLinked to original sources

bioRxiv2026

H3K9me3 as a gatekeeper of lineage-specific enhancers in embryonic progenitor cells

While many studies of developmental control have focused on gene activation, less is known about the extent to which regulatory programs are actively repressed in progenitor cells. We previously showed that trimethylation of histone H3 at lysine 9 (H3K9me3) is a repressive mark that is remodeled on protein-coding genes when endodermal progenitors transition to liver and pancreatic {beta} cell fates. Yet whether H3K9me3 is dynamic at promoters and enhancers has not been determined. Here we find that promoters of liver-specific genes are strongly enriched for H3K9me3 in undifferentiated progenitors, whereas such enrichment is not observed at promoters of more broadly expressed liver genes. We further show that enhancers specific to differentiated tissues--including liver, islet, and cerebral cortex--are strongly enriched for H3K9me3 in their corresponding tissue stem and progenitor cells. In hepatoblasts, H3K9me3 contributes to maintaining the undifferentiated state by restricting FOXA2 and HNF4 from binding to most enhancers, while there remain thousands of H3K9me3-marked enhancers where the factors are not restricted from binding. Our findings illustrate how H3K9me3-mediated heterochromatinization can restrict transcription factor engagement in progenitor cells to prevent inappropriate activation during early development. H3K9me3 at enhancers that allow transcription factor binding may reflect developmental competence.

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↗

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↗

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↗

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↗