Biology subjects

Pankajam, A. V.

Publications and source records attributed to Pankajam, A. V..

7 recordsLinked to original sources

bioRxiv2026

An Integrated Multi-omics Single Cell Atlas of the Human RPE and Choroid

The retinal pigment epithelium and choroid are critical for supporting the function and maintaining the homeostasis of the outer retina, and their dysfunction underlies a range of inherited and complex ocular diseases. To comprehensively characterize the cellular, transcriptomic, and epigenomic heterogeneity and dynamics within these tissues, we assembled an integrated multi-omics reference atlas comprising 719,813 single-cell/single-nucleus transcriptomes and 234,007 snATAC-seq profiles from 102 ancestrally diverse donors spanning 0 to 99 years of age, including cells from both the macula and periphery. This atlas resolves 48 distinct cell types or states and catalogs 448,567 open chromatin regions. Specifically, we resolved five distinct RPE subpopulations organized along a central-to-peripheral spatial axis, alongside two distinct stress/senescence states. We reconstructed the transcriptomic and epigenetic zonation of endothelial cells and expanded choroidal stromal heterogeneity by characterizing 11 fibroblast and two pericyte types. Age-associated compositional analysis revealed a significant fractional depletion of melanocytes, PI16+ fibroblasts, and venule endothelial cells with age, alongside a modest relative loss of central RPE and a corresponding increase in far-peripheral RPE. Cell-type-specific aging transcriptomics uncovered shared pathways related to inflammatory responses alongside distinct cell-type-specific signatures. Notably, significant age-associated epigenetic changes concentrated in the macula during the transition from early-to-middle adulthood and remained stable into old age, with transcription factors from the AP-1/bZIP family emerging as the dominant enriched motifs. Finally, integrating this atlas with AMD GWAS data provides novel variant-to-gene evidence implicating LIPG and COL4A3 in AMD pathogenesis. Together, this multi-omics atlas serves as both an invaluable community reference and a powerful discovery engine that translates genetic risk signals into localized target cells and candidate mechanisms, laying a foundation for understanding RPE/choroid biology in health and disease.

genomics↗

bioRxiv2026

Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus

The variety of cell phenotypes identified by single-cell technologies is rapidly expanding, yet this knowledge is dispersed across the scientific literature and incompletely represented in structured resources. We present the CellLink corpus, a manually annotated collection of over 22,000 mentions of human and mouse cell populations in recent journal articles, distinguishing specific cell phenotypes, heterogeneous cell populations, and vague cell populations, and linking to Cell Ontology (CL) terms as either exact or related matches, covering nearly half of the terms in the current CL. A systematic analysis reveals lineage-specific patterns in how authors utilize anatomical context, molecular signatures, functional roles, developmental stage, and other attributes in cell naming. We show that fine-tuning transformer-based models on CellLink yields strong performance for named entity recognition, while embedding-based approaches support zero-shot entity linking and distinguishing exact from related matches. We further demonstrate the utility of CellLink to expand and refine the chondrocyte branch of CL.

bioinformatics↗

bioRxiv2025

Benchmarking single cell transcriptome matching methods for incremental growth of reference atlases

BackgroundThe advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas for healthy human cells. Many multi-faceted cell atlases for different organs, species, and diseases now exist, though challenges remain in harmonizing cell types and unifying nomenclature among respective cell atlases. Multiple machine learning and artificial intelligence methods, including models pre-trained on large-scale cell atlas datasets, are publicly available for single cell community users to computationally map their cell clusters to the cell atlases. ResultsThis study benchmarks seven computational tools for cell type matching and label transfer - Azimuth, CellTypist, CellHint, FR-Match, scArches, scPred, and singleR - in ten organ systems. Using healthy lung as an exemplary organ, when matching the well-annotated cell types in two atlases - the Human Lung Cell Atlas (HLCA) and the LungMAP Single-Cell Reference (CellRef), variations in the matching accuracy were observed, especially in rare cell types, underlining the need for a consensus strategy using a selective set of computational methods. In the meta-analysis, the benchmarked methods were used to incrementally integrate 61 cell types from HLCA and 48 from CellRef, resulting in a cell meta-atlas of 41 matched, 20 HLCA-, and 7 CellRef-specific cell types. Similar approach revealed 25 matched cell types existed in two independent kidney atlases. Generalizability of the benchmarking performances were further demonstrated in a variety of organ systems. ConclusionThis study reveals complementing strengths of the benchmarked methods and presents a framework for incremental growth of cell types in cell atlases.

bioinformatics↗

bioRxiv2024

Discovery of cell type classification marker genes from single cell RNA sequencing data using NS-Forest

The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements to select marker gene combinations that exhibit highly selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to 1, with a metric of 1 assigned to markers that are only expressed within their target cell types and not in cells of any other cell types. NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches at classification with significantly higher F-beta scores when applied to datasets from three human organs - brain, kidney, and lung.

bioinformatics↗

bioRxiv2023

Comprehensive mapping of cell fates in microsatellite unstable cancer cells support dual targeting of WRN and ATR

Addiction to the WRN helicase is a unique vulnerability of human cancers with high levels of microsatellite instability (MSI-H). However, while prolonged loss of WRN ultimately leads to cell death, little is known about how MSI-H cancers initially respond to acute loss of WRN, knowledge that would be helpful for informing clinical development of WRN-targeting therapy, predicting possible resistance mechanisms, and identifying useful biomarkers of successful WRN inhibition. Here, we report the construction of an inducible ligand-mediated degradation system wherein the stability of endogenous WRN protein can be rapidly and specifically tuned, enabling us to track the complete sequence of cellular events elicited by acute loss of WRN function. We find that WRN degradation leads to immediate accrual of DNA damage in a replication-dependent manner that curiously did not robustly engage checkpoint mechanisms to halt DNA synthesis. As a result, WRN-degraded MSI-H cancer cells accumulate DNA damage across multiple replicative cycles and undergo successive rounds of increasingly aberrant mitoses, ultimately triggering cell death. Of potential therapeutic importance, we find no evidence of any generalized mechanism by which MSI-H cancers could adapt to near-complete loss of WRN. However, under conditions of partial WRN degradation, addition of low dose ATR inhibitor significantly increased their combined efficacy to levels approaching full inactivation of WRN. Overall, our results provided the first comprehensive view of molecular events linking upstream inhibition of WRN to subsequent cell death and suggested a potential therapeutical rationale for dual targeting of WRN and ATR.

cancer biology↗

bioRxiv2020

The bakerss yeast Msh4-Msh5 associates with double-strand break hotspots and chromosome axis during meiosis to promote crossovers

Segregation of homologous chromosomes during the first meiotic division requires at least one obligate crossover/exchange event between the homolog pairs. In the bakers yeast Saccharomyces cerevisiae and mammals, the mismatch repair-related factors, Msh4-Msh5 and Mlh1-Mlh3 generate the majority of the meiotic crossovers from programmed double-strand breaks (DSBs). To understand the mechanistic role of Msh4-Msh5 in meiotic crossing over, we performed genome-wide ChIP-sequencing and cytological analysis of the Msh5 protein in cells synchronized for meiosis. We observe that Msh5 associates with DSB hotspots, chromosome axis, and centromeres. We found that the initial recruitment of Msh4-Msh5 occurs following DSB resection. A two-step Msh5 binding pattern was observed: an early weak binding at DSB hotspots followed by enhanced late binding upon the formation of double Holliday junction structures. Msh5 association with the chromosome axis is Red1 dependent, while Msh5 association with the DSB hotspots and axis is dependent on DSB formation by Spo11. Msh5 binding was enhanced at strong DSB hotspots consistent with a role for DSB frequency in promoting Msh5 binding. These data on the in vivo localization of Msh5 during meiosis have implications for how Msh4-Msh5 may work with other crossover and synapsis promoting factors to ensure Holliday junction resolution at the chromosome axis. AUTHOR SUMMARYDuring meiosis, crossovers facilitate physical linkages between homologous chromosomes that ensure their accurate segregation. Meiotic crossovers are initiated from programmed DNA double-strand breaks (DSBs). In the bakers yeast and mammals, DSBs are repaired into crossovers primarily through a pathway involving the highly conserved mismatch repair related Msh4-Msh5 complex along with other crossover promoting factors. In vitro and physical studies suggest that the Msh4-Msh5 heterodimer facilitates meiotic crossover formation by stabilizing Holliday junctions. We investigated the genome-wide in vivo binding sites of Msh5 during meiotic progression. Msh5 was enriched at DSB hotspots, chromosome axis, and centromere sites. Our results suggest Msh5 associates with both DSB sites on the chromosomal loops and with the chromosome axis to promote crossover formation. These results on the in vivo dynamic localization of the Msh5 protein provide novel insights into how the Msh4-Msh5 complex may work with other crossover and synapsis promoting factors to facilitate crossover formation.

genetics↗

bioRxiv2018

The Neurospora crassa standard Oak Ridge background exhibits an atypically efficient meiotic silencing by unpaired DNA.

Meiotic silencing by unpaired DNA (MSUD) was discovered in crosses made in the standard Oak Ridge (OR) genetic background of Neurospora crassa. However, MSUD often was decidedly less efficient when the OR-derived MSUD tester strains were crossed with wild-isolated strains (W), which suggested either that sequence heterozygosity in tester x W crosses suppresses MSUD, or that OR represents the MSUD-conducive extreme in the range of genetic variation in MSUD efficiency. Our results support the latter model. MSUD was much less efficient in near-isogenic crosses made in a novel N. crassa B/S1 and the N. tetrasperma 85 genetic backgrounds. Possibly, regulatory cues that in other genetic backgrounds calibrate the MSUD response are missing from OR. The OR versus B/S1 difference appears to be determined by loci on chromosomes 1, 2, and 5. OR crosses heterozygous for a duplicated chromosome segment (Dp) have for long been known to exhibit an MSUD-dependent barren phenotype. However, inefficient MSUD in N. tetrasperma 85 made Dp-heterozygous crosses non-barren. This is germane to our earlier demonstration that Dps can act as dominant suppressors of repeat-induced point mutation (RIP). Occasionally, during ascospore partitioning rare asci contained >8 nuclei, and round ascospores dispersed less efficiently than spindle-shaped ones.\n\nGeneral abstractIn crosses made in the standard OR genetic background of Neurospora crassa, an RNAi-mediated process called MSUD efficiently silences any gene not properly paired with its homologue during meiosis. We found that MSUD was not as efficient in comparable crosses made in the N. crassa B/S1 and N. tetrasperma 85 backgrounds, suggesting that efficient MSUD is not necessarily the norm in Neurospora. Indeed, using OR strains for genetic studies probably fortuitously facilitated the discovery of MSUD and its suppressors. As few as three unlinked loci appear to underlie the OR versus B/S1 difference in MSUD.

genetics↗