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Pittu, A.

Publications and source records attributed to Pittu, A..

2 recordsLinked to original sources

Treatment Resistant Persister Cells Exploit Macrophage Lipid Metabolism to Sustain Glioblastoma Growth

Glioblastoma (GBM) displays pronounced intratumoral heterogeneity, posing significant challenges to understanding its biology and developing effective treatments. Using spatial multi-omics, in vivo functional assays, and systems-level analysis, we delineate the diverse metabolic and immune architecture of GBM. We identify a lipid-dependent lineage of treatment-resistant persister cells (TRPCs) that engage tumor-associated macrophages (TAMs) in a spatially organized, metabolically specialized crosstalk. TRPCs co-opt CCR2, CSF1R, CD163+ TAMs for lipid scavenging and acquisition, promoting a pro-tumorigenic and immunosuppressive microenvironment. This cooperative axis is critically dependent on lipid chaperones like FABPs, whose targeting disrupts TAM recruitment, remodels immune composition, and suppresses tumor growth. Retrospective clinical analyses reveal that elevated TRPC-associated transcriptome may serve as stratification criteria to identify patients benefiting from lipid-lowering therapies like statins. Our findings uncover a targetable immunometabolic circuit between TRPCs and TAMs and support the development of precision therapies that disrupt lipid-fueled tumor-immune cooperation in GBM. In briefTreatment-resistant persister cells (TRPCs) in glioblastoma spatially engage TAMs to facilitate lipid transfer, thereby sustaining tumor growth and promoting immune evasion. Targeting this TRPC-TAM metabolic axis reprograms the immunosuppressive microenvironment and improves therapeutic outcomes, revealing a clinically actionable metabolic vulnerability with potential for precision immune-metabolic interventions in GBM. HighlightsO_LIGBM exhibits spatially resolved heterogeneity revealing correlative metabolic and immune micro-niches C_LIO_LITRPC lineage promotes a pro-tumorigenic and immunosuppressive microenvironment by recruiting lipid-specialized TAMs C_LIO_LITRPC lineage hijacks TAMs for metabolic support via stimulating lipid transfer and acquisition C_LIO_LIDisruption of the TRPC-TAM lipid axis, including through FABP3 targeting, reprograms the immune landscape, limits TAM recruitment, and impairs tumor progression C_LIO_LITRPC lineage and associated micro-niche transcriptomic profiles can serve as criteria for patient stratification and identification of responders to lipid-lowering therapies, such as statins C_LI

cancer biology↗

Get to know your neighbors with a SNAQ™: A framework for single cell spatial neighborhood analysis in immunohistochemical images

MotivationAnalyzing the local microenvironment of tumor cells can provide significant insights into their complex interactions with their cellular surroundings, including immune cells. By quantifying the prevalence and distances of certain immune cells in the vicinity of tumor cells through a neighborhood analysis, patterns may emerge that indicate specific associations between cell populations. Such analyses can reveal important aspects of tumor-immune dynamics, which may inform therapeutic strategies. This method enables an in-depth exploration of spatial interactions among different cell types, which is crucial for research in oncology, immunology, and developmental biology. ResultsWe introduce an R Markdown script called SNAQTM (Single-cell Spatial Neighborhood Analysis and Quantification), which conducts a neighborhood analysis on immunofluorescent images without the need for extensive coding knowledge. As a demonstration, SNAQTM was used to analyze images of pancreatic ductal adenocarcinoma. Samples stained for DAPI, PanCK, CD68, and PD-L1 were segmented and classified using QuPath. The resulting CSV files were exported into RStudio for further analysis and visualization using SNAQTM. Visualizations include plots revealing the cellular composition of neighborhoods around multiple cell types within a customizable radius. Additionally, the analysis includes measuring the distances between cells of certain types relative to others across multiple regions of interest. Availability and implementationThe R Markdown files that comprise the SNAQTM algorithm and the input data from this paper are freely available on the web at https://github.com/AryehSilver1/SNAQ. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/606539v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1cda240org.highwire.dtl.DTLVardef@157280forg.highwire.dtl.DTLVardef@1072cf8org.highwire.dtl.DTLVardef@1964e0_HPS_FORMAT_FIGEXP M_FIG C_FIG Created with BioRender.com.

bioinformatics↗