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Biology subjects

Dharmadhikari, G.

Publications and source records attributed to Dharmadhikari, G..

2 recordsLinked to original sources

Cancer-myeloid cell invasive program in pediatric-type diffuse high-grade glioma

Pediatric-type diffuse high-grade gliomas (pHGGs) are aggressive, heterogeneous brain tumors shaped by intricate cancer-microenvironment cell-cell interactions. Here, we present an integrative multimodal pHGGmap, encompassing over 800,000 cells from 136 patients profiled across transcriptomic, epigenomic, and spatial modalities. Its analysis delineated robust cancer-myeloid cell programs that structured the tumor ecosystem and identified ten distinct cancer cell states, including previously unrecognized developmental and context-responsive programs. Among these, radial glial-like (RG-like) cells exhibited dual stress-adapted and infiltrative phenotypes. Tumor-associated monocyte-derived macrophages and resident microglia engaged in four distinct immunomodulatory programs aligned with specific cancer states. Three conserved multicellular communities were maintained across treatment, including a stable, spatially and transcriptionally linked RG-like/complement-macrophage niche, indicative of cellular co-option and adaptation to support invasion. Longitudinal profiling of a metastatic diffuse midline glioma case showed that RG-like cells predominate during dissemination and remain associated with complement-enriched macrophages, whose reprogramming restores immune activation. pHGGmap establishes a landmark resource for translational discovery. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/701142v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@7ae699org.highwire.dtl.DTLVardef@b95b92org.highwire.dtl.DTLVardef@12adcf9org.highwire.dtl.DTLVardef@1118703_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Cell type purification by single-cell transcriptome-trained sorting

Traditional cell type enrichment using fluorescence activated cell sorting (FACS) relies on methods that specifically label the cell type of interest. Here we propose GateID, a computational method that combines single-cell transcriptomics for unbiased cell type identification with FACS index sorting to purify cell types of choice. We validate GateID by purifying various cell types from the zebrafish kidney marrow and the human pancreas without resorting to specific antibodies or transgenes.

bioinformatics↗