Biology subjects

Prabakar, R. K.

Publications and source records attributed to Prabakar, R. K..

5 recordsLinked to original sources

bioRxiv2026

Gene regulatory logic underlying the differentiation of mixed lineages in human thymic epithelial cells

The thymus enacts central tolerance through thymic (TECs), which present self-antigens to developing T cells self-reactive cells, preventing autoimmunity. Human TECs include populations that express lineage-committed programs corresponding to distinct peripheral tissues, yet how they acquire these programs and whether these TEC subpopulations are genetically related and conserved in mice remain unknown. Using matched single-cell gene-expression and chromatin-accessibility profiling of 126,687 human TECs, we identified 17 TEC subsets defined by their distinct expression profiles of 1,843 cell type restricted-antigens (CRAs) from 48 peripheral cell types. With a new phylogenetic reconstruction method, we show that lineage-committed TECs share a common ancestor, retain a thymus-specific mixed identity through co-expression of multiple CRA signatures, and are specified by coordinated activator and repressor regulons. We also identified a basement-membrane-collagen-rich state (BMC-TECs) with progenitor-like properties. Finally, mouse TECs shared the regulatory strategy but conserved few master transcription-factor drivers. This study mechanistically characterizes human TEC subpopulations and reveals the gene regulatory logic that leads to their distinct diversification and mixed identities in humans and mice.

immunology↗

bioRxiv2025

Sparse, random sampling is sufficient for central tolerance

Negative selection in the thymus limits autoimmunity by eliminating T cells that react strongly to self. Individual T cells, however, are only exposed to a small fraction of all self peptides during their "training" in the thymus, and it is puzzling how tolerance can be generalized to the remaining "test" self peptides across peripheral tissues in the body. Using a machine learning perspective, we show that such generalization is possible because the immune system satisfies two conditions: first that peptide abundance levels in the human thymus and periphery are highly correlated (i.e., training distribution {approx} test distribution), and second that cross-reactivity allows T cells to effectively learn binding information of similar peptides without explicitly interacting with all of them. Together, we show that sparse, random sampling of only 10% of self peptides in the thymus is sufficient to avoid reactivity to 90% of peripheral self, and we support this result with diverse experimental data. We then validate two predictions by our model; the first is that only 200-250 antigen presenting cells need to be seen by a T cell to ensure its robust selection, and the second relates how peptides missing from the thymus can drive auto-immunity of peripheral tissues. Overall, we provide a plausible answer to a long-standing question underlying adaptive immunity, and we highlight how generalization, a fundamental challenge faced by nearly every learning algorithm, is uniquely tackled by the immune system.

immunology↗

bioRxiv2024

GeTMoR: Simultaneous genomic, transcriptomic, andmorphological profiling of rare single cells

1Circulating tumor cells (CTCs), and circulating tumor related cells, are extremely rare cells that intravasate from the tumor into the circulatory system and can be captured via a liquid biopsy. Although CTCs contribute to the metastatic cascade, and diverse phenotypes of CTCs have been observed - including the cytokeratin expressing CTCs, CTC clusters, large polyploid CTCs, and CTCs undergoing epithelial to mesenchymal transition - little is known about their functionality. By virtue of CTCs being rare, a detection method that maximizes the information obtained per cell would be ideal to understand their biology and for use in diagnostic approaches. The challenge is that rare cell detection necessitates extensive processing steps, during which molecular content, such as RNA and DNA, needs to be preserved for downstream single cell analysis. We developed GEnomic, Transcriptomic, and MOrphological profiling of Rare cells (GeTMoR), a method that extends the High Definition Single Cell Assay for detecting rare cancer related cells to simultaneously image and profile the genome and transcriptome from single rare cells. We validated GeTMoR by spiking in cancer cell lines into whole blood to evaluate the quality of recovered gene expression and copy number profile from the same cell. The GeTMoR approach provides the ability to link the phenotype of rare cells, including CTCs, to their genome and transcriptome, thereby enabling insight into rare cell biology.

genomics↗

bioRxiv2024

Polyploid cancer cells reveal signatures of chemotherapy resistance

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression- free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.

cancer biology↗

bioRxiv2018

SMURF-seq for fast, multiplexed copy number profiling with long-read sequencers

We present SMURF-seq, a protocol to efficiently sequence short DNA molecules on a long-read sequencer by randomly ligating them to form long molecules. Applying SMURF-seq using Oxford Nanopore Technologies MinION yields up to 30 countable fragments per read at present, which generates multiple copy number profiles in a single run at a reduced time and cost. More broadly, SMURF-seq expands the utility of long-read sequencers for read-counting applications, which do not benefit from increased read length.

genomics↗