Biology subjects

Engleman, E.

Publications and source records attributed to Engleman, E..

3 recordsLinked to original sources

bioRxiv2025

Mitochondria redistribution organizes the immunosuppressive tumor ecosystem

Hostile conditions in the tumor microenvironment restrict cellular respiration, yet mitochondrial metabolism remains indispensable for tumor growth and the activity of immunosuppressive cells. How tumor ecosystems sustain mitochondrial output has been unclear. Here, we show that cancer cells resolve this paradox by acting as hubs of intercellular mitochondrial redistribution. Using mitochondrial reporter systems, we demonstrate that cancer cells import host-derived mitochondria, integrate them into their endogenous network, and subsequently relay these hybrid organelles to neighboring immune cells. Mitochondria redistribution reprograms recipient neutrophils, macrophages, and CD4+ T cells into highly suppressive states but drives CD8+ T cell exhaustion. Within cancer cells, fusion of incoming mitochondria induces filamentous P5CS assembly, enhances biosynthetic output, and enables the refurbishment of damaged organelles into fully functional units. Disrupting mitochondrial redistribution collapses the immunosuppressive ecosystem and impairs tumor growth. Thus, cancer cells do not hoard resources but orchestrate a redistribution program that fortifies their own metabolic resilience, derails anti-tumor immunity, and sustains immunosuppressive partners. HIGHLIGHTSO_LITumor cells regulate their ecosystem by redistributing mitochondria C_LIO_LIRedistributed mitochondria expand immunosuppressive cells but exhausts CD8+ T cells C_LIO_LIMitochondria fusion within cancer cells, which precedes redistribution, optimizes metabolic output by triggering conformational changes in P5CS C_LIO_LIMitochondria fusion allows cancer cells to incorporate and refurbish seemingly incompetent host-derived mitochondria, improving efficiency in the tumor ecosystem C_LI

cancer biology↗

bioRxiv2020

G-baToN: a versatile cell-cell interaction reporter system.

Cell-cell interactions influence all aspects of development, homeostasis, and disease. In cancer, interactions between cancer cells and stromal cells play a major role in nearly every step of carcinogenesis. Thus, the ability to record cell-cell interactions would facilitate mechanistic delineation of the role of cancer microenvironment. Here, we describe GFP-based Touching Nexus (G-baToN) which relies upon nanobody-directed fluorescent protein transfer to enable sensitive and specific labeling of cells after cell-cell interactions. G-baToN is a generalizable system that enables physical contact-based labeling between various cell types, including diverse cancer-stromal cell pairs. A suite of orthogonal baToN tools enables reciprocal cell-cell labeling, interaction-dependent cargo transfer, and the identification of higher-order cell-cell interactions across a wide range of cell types. The ability to track physically interacting cells with these simple and sensitive systems will greatly accelerate our understanding of the outputs of cell-cell interactions in cancer as well as across many biological processes.

bioengineering↗

bioRxiv2017

Leveraging heterogeneity across multiple data sets increases accuracy of cell-mixture deconvolution and reduces biological and technical biases

In silico quantification of cell proportions from mixed-cell transcriptomics data (deconvolution) requires a reference expression matrix, called basis matrix. We hypothesized that matrices created using only healthy samples from a single microarray platform would introduce biological and technical biases in deconvolution. We show presence of such biases in two existing matrices, IRIS and LM22, irrespective of the deconvolution method used. Here, we present immunoStates, a basis matrix built using 6160 samples with different disease states across 42 microarray platforms. We found that immunoStates significantly reduced biological and technical biases. We further show that cellular proportion estimates using immunoStates are consistently more correlated with measured proportions than IRIS and LM22, across all methods. Importantly, we found that different methods have virtually no effect once the basis matrix is chosen. Our results demonstrate the need and importance of incorporating biological and technical heterogeneity in a basis matrix for achieving consistently high accuracy.

bioinformatics↗