Biology subjects

Lander, E.

Publications and source records attributed to Lander, E..

3 recordsLinked to original sources

bioRxiv2025

PD-1 blockade during T cell priming enhances long-term protection against metastatic tumors by epigenetically tuning T cell exhaustion

In cancer, CD8+ T cell responses are dominated by exhausted T cells, which can be reinvigorated using immune checkpoint blockade therapy and can control large tumors. However, it remains unclear which T cell fate best supports long-term immunity following tumor regression or clearance and a period of minimal antigen load. This question is particularly relevant following surgical tumor resection, when tuning the immune system could prevent recurrence. To determine which T cell fate provides durable protection following surgery and metastatic rechallenge, we modulated T cell priming using anti-PD-1, IFN-{beta} or agonistic anti-CD40 and assessed effects on CD8+ T cell differentiation and overall survival. IFN-{beta} and anti-CD40 promoted effector and memory-like T cell states, respectively, whereas anti-PD-1 did not markedly alter T cell differentiation, yet conferred the greatest survival benefit against metastatic tumors. Notably, anti-PD-1 induced epigenetic remodeling, which was detectable upon metastatic recall, consistent with the maintenance of a circulatory intermediate-exhausted T cell state. Thus, while effector and memory precursor-like T cells could be generated with IFN-{beta} and agonistic anti-CD40, only the intermediate-exhausted T cell state driven by anti-PD-1 supported durable anti-tumor immunity. SummaryThis study shows that PD-1 blockade during T cell priming promotes a circulatory intermediate-exhausted CD8 T cell state that uniquely supports durable anti-tumor immunity after surgical resection and metastatic challenge, outperforming effector or memory-like T cell responses generated by IFN-{beta} or CD40 agonist treatment, respectively.

immunology↗

bioRxiv2017

Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

bioinformatics↗

bioRxiv2017

Composite measurements and molecular compressed sensing for highly efficient transcriptomics

RNA profiling is an excellent phenotype of cellular responses and tissue states, but can be costly to generate at the massive scale required for studies of regulatory circuits, genetic states or perturbation screens. Here, we draw on a series of advances over the last decade in the field of mathematics to establish a rigorous link between biological structure, data compressibility, and efficient data acquisition. We propose that very few random composite measurements - in which gene abundances are combined in a random linear combination - are needed to approximate the high-dimensional similarity between any pair of gene abundance profiles. We then show how finding latent, sparse representations of gene expression data would enable us to \"decompress\" a small number of random composite measurements and recover high-dimensional gene expression levels that were not measured (unobserved). We present a new algorithm for finding sparse, modular structure, which improves the ability to interpret samples in terms of small numbers of active modules, and show that the modular structure we find is sufficient to recover gene expression profiles from composite measurements (with ~100-fold fewer composite measurements than genes). Moreover, the knowledge that sparse, modular structures exist allows us to recover expression profiles from composite measurements, even without access to any training data. Finally, we present a proof-of-concept experiment for making composite measurements in the laboratory, involving the measurement of linear combinations of RNA abundances. Altogether, our results suggest new compressive modalities in experimental biology that can form a foundation for massive scaling in high-throughput measurements, while also offering new insights into the interpretation of high-dimensional data.

systems biology↗