Biology subjects

Biermann, J.

Publications and source records attributed to Biermann, J..

4 recordsLinked to original sources

bioRxiv2022

KINOMO: A non-negative matrix factorization framework for recovering intra- and inter-tumoral heterogeneity from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) is a powerful technology to uncover cellular heterogeneity in tumor ecosystems. Due to differences in underlying gene load, direct comparison between patient samples is challenging, and this is further complicated by the sparsity of data matrices in scRNA-seq. Here, we present a factorization method called KINOMO (Kernel dIfferentiability correlation-based NOn-negative Matrix factorization algorithm using Kullback-Leibler divergence loss Optimization). This tool uses quadratic approximation approach for error correction and an iterative multiplicative approach, which improves the quality assessment of NMF-identified factorization, while mitigating biases introduced by inter-patient genomic variability. We benchmarked this new approach against nine different methods across 15 scRNA-seq experiments and find that KINOMO outperforms prior methods when evaluated with an adjusted Rand index (ARI), ranging 0.82-0.91 compared to 0.68-0.77. Thus, KINOMO provides an improved approach for determining coherent transcriptional programs (and meta-programs) from scRNA-seq data of cancer tissues, enabling comparison of patients with variable genomic backgrounds. ClassificationPhysical Sciences (Applied Mathematics; Biophysics and Computational Biology), Biological Sciences (Applied Biological Sciences; Biophysics and Computational Biology; Medical Sciences; Systems Biology.). Significance StatementIdentification of shared or distinct cell programs in single-cell RNA-seq data of patient cancer cells is challenging due to underlying variability of gene load which determines transcriptional output. We developed an analytical approach to define transcriptional variability more accurately across patients and therefore enable comparison of program expression despite inherent genetic heterogeneity. Thus, this method overcomes challenges not adequately addressed by other methods broadly used for the analysis of single-cell genomics data.

systems biology↗

bioRxiv2022

The CD58:CD2 axis is co-regulated with PD-L1 via CMTM6 and governs anti-tumor immunity

The cell autonomous balance of immune-inhibitory and -stimulatory signals is a critical yet poorly understood process in cancer immune evasion. Using patient-derived co-culture models and humanized mouse models, we show that an intact CD58:CD2 interaction is necessary for anti-tumor immunity. Defects in this axis lead to multi-faceted immune evasion through impaired CD2-dependent T cell polyfunctionality, T cell exclusion, impaired intra-tumoral proliferation, and concurrent protein stabilization of PD-L1. We performed genome-scale CRISPR-Cas9 and CD58 coimmunoprecipitation mass spectrometry screens identifying CMTM6 as a key stabilizer of CD58, and show that CMTM6 is required for concurrent upregulation of PD-L1 in CD58 loss. Single-cell RNA-seq analysis of patient melanoma samples demonstrates that most TILs lack expression of primary costimulatory signals required for response to PD-1 blockade (e.g. CD28), but maintain strong CD2 expression, thus providing an opportunity to mobilize a so far therapeutically untapped pool of TILs for anti-tumor immunity. We identify two potential therapeutic avenues, including rescued activation of human CD2-expressing TILs using recombinant CD58 protein, and targeted disruption of PD-L1/CMTM6 interactions. Our work identifies an underappreciated yet critical axis at the nexus of cancer immunity and evasion, uncovers a fundamental mechanism of co-inhibitory and -stimulatory signal balancing, and provides new approaches to improving cancer immunotherapies.

cancer biology↗

bioRxiv2022

Alveolar regeneration following viral infection is independent of tuft cells

Severe injuries following viral infection cause lung epithelial destruction with the presence of ectopic basal progenitor cells (EBCs), although the exact function of EBCs remains controversial. We and others previously showed the presence of ectopic tuft cells in the disrupted alveolar region following severe influenza infection. Here, we further revealed that the ectopic tuft cells are derived from EBCs. This process is amplified by Wnt signaling inhibition but suppressed by Notch inhibition. Further analysis revealed that p63-CreER labeled population de novo arising during regeneration includes alveolar epithelial cells when Tamoxifen was administrated after viral infection. The generation of the p63-CreER labeled alveolar cells is independent of tuft cells, demonstrating segregated differentiation paths of EBCs in lung repair. EBCs and ectopic tuft cells can also be found in the lung parenchyma post SARS-CoV-2 infection, suggesting a similar response to severe injuries in humans.

cell biology↗

bioRxiv2022

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Single-cell genomics are enabling technologies, but their broad clinical application remains challenging. We report an easily adaptable approach for single-cell transcriptome and T cell receptor (TCR)-sequencing, and matched whole-genome sequencing from tiny, frozen clinical specimens. We achieve similar quality and biological outputs while reducing artifactual signals compared to data from matched fresh tissue samples. Profiling sequentially collected melanoma samples from the KEYNOTE-001 trial, we resolve cellular, genomic, and clonotype dynamics that encapsulate molecular patterns of tumor evolution during anti-PD-1 therapy. To demonstrate applicability to banked biospecimens of rare diseases, we generate a large uveal melanoma liver metastasis single-cell and matched WGS atlas, which revealed niche-specific impairment of clonal T cell expansion. This study provides a foundational framework for propelling single-cell genomics to the clinical arena.

genomics↗