Biology subjects

Jindal, K.

Publications and source records attributed to Jindal, K..

5 recordsLinked to original sources

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

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

Network Analysis and Human Single Cell Brain Transcriptomics Reveal Novel Aspects of Alpha-Synuclein (SNCA) Biology

Alpha-synuclein (SNCA) aggregates are pathological hallmarks of synucleinopathies, neurodegenerative disorders including Parkinsons Disease (PD) and Lewy Body Dementia (LBD). Functional networks are not yet well-characterized for SNCA by CNS cell type. We investigated cell-specific differences in SNCA expression using Allen Brain Database single-nucleus RNA-seq data from human Middle Temporal Gyrus (MTG, 15,928 nuclei) and Anterior Cingulate Cortex (ACC, 7,258 nuclei). Weighted gene co-expression analysis (WGCNA) and hierarchical clustering identified a conserved SNCA co-expression module. Module genes were highly conserved (p < 10-10) and most highly expressed in excitatory neurons versus inhibitory neurons and other glial cells. SNCA co-expression module genes from ACC and MTG regions were then used to construct a protein-protein interaction (PPI) network, with SNCA empirically top hub. Genes in the SNCA PPI network were compared with genes nearest single nucleotide polymorphisms linked with PD risk in genome-wide association studies. 16 genes in our PPI network are nearest genes to PD risk loci (p < 0.0006) and 55 genes map within 100kb. Selected SNCA PPI network genes nearest PD risk loci were disrupted by CRISPR knock out gene editing for validation of network functional significance; disruption of STK39, GBA, and MBNL2 resulted in significantly elevated intracellular SNCA expression.

neuroscience↗

bioRxiv2018

Barcoded oligonucleotides ligated on RNA amplified for multiplex and parallel in-situ analyses

We present Barcoded Oligonucleotides Ligated On RNA Amplified for Multiplexed and parallel In-Situ analysis (BOLORAMIS), a reverse-transcription (RT)-free method for spatially-resolved, targeted, in-situ RNA identification of single or multiple targets. For this proof of concept, we have profiled 154 distinct coding and small non-coding transcripts ranging in sizes 18 nucleotides in length and upwards, from over 200, 000 individual human induced pluripotent stem cells (iPSC) and demonstrated compatibility with multiplexed detection, enabled by fluorescent in-situ sequencing. We use BOLORAMIS data to identify differences in spatial localization and cell-to-cell expression heterogeneity. Our results demonstrate BOLORAMIS to be a generalizable toolset for targeted, in-situ detection of coding and small non-coding RNA for single or multiplexed applications.

cell biology↗