Biology subjects

Haymaker, C.

Publications and source records attributed to Haymaker, C..

5 recordsLinked to original sources

bioRxiv2024

TLS_Finder: An algorithm for Identifying Tertiary Lymphoid Structures Using Immune Cell Spatial Coordinates

Tertiary lymphoid structures (TLS) are lymphoid formations that develop in non-lymphoid tissues during chronic inflammation, autoimmune diseases, and cancer. Accurate identification and quantification of TLS in tissue can provide crucial insights into the immune response of several disease processes including antitumor immune response. TLS are defined as aggregates of T cells, B cells and dendritic cells. In histological tissue sections stained with Hematoxylin and Eosin they are identified as aggregates of 50 or more lymphoid cells, however immunohistochemical analysis are required to confirm presence of distinct immune cell patterns. Assessment of lymphoid aggregates can be done in H&E slides or in slides stained with single or multiplex immunohistochemistry or other tissue based high-plex approaches with key biomarkers such as CD3 (T cells) and CD20 (B cells), however manual assessment of them is time consuming thus limiting its full evaluation. To our knowledge, published algorithms that identify TLS within a tissue are based on histological assessment of H&E slides or through use of ML algorithms trained on images that show the TLS presence in tissues; and quantification and spatial analysis of TLS still remains a challenge. This study aims to develop a robust algorithm to recognize TLS using the spatial coordinates of immune cells in any given tissue. The algorithm uses X and Y coordinates of T and B cells in tissues identified by a pathologist-supervised digital image analysis of multiplex chromogenic immunohistochemistry of tissues stained with CD3 and CD20. The algorithm is flexible to be used for detailed analysis of TLS stages; including other cell types within the definition of TLS; such as dendritic cells (DC) and high endothelial venules (HEV); to assess different stages of TLS formation.

bioinformatics↗

bioRxiv2024

Comparison of imaging-based single-cell resolution spatial transcriptomics profiling platforms using formalin-fixed, paraffin-embedded tumor samples

Imaging-based spatial transcriptomics (ST) is evolving rapidly as a pivotal technology in studying the biology of tumors and their associated microenvironments. However, the strengths of the commercially available ST platforms in studying spatial biology have not been systematically evaluated using rigorously controlled experiments. In this study, we used serial 5-m sections of formalin-fixed, paraffin-embedded surgically resected lung adenocarcinoma and pleural mesothelioma tumor samples in tissue microarrays to compare the performance of the single cell ST platforms CosMx, MERFISH, and Xenium (uni/multi-modal) platforms in reference to bulk RNA sequencing, multiplex immunofluorescence, GeoMx Digital Spatial Profiler, and hematoxylin and eosin staining data for the same samples. In addition to objective assessment of automatic cell segmentation and phenotyping, we performed pixel-resolution manual evaluation of phenotyping to carry out pathologically meaningful comparison between ST platforms. Our study detailed the intricate differences between the ST platforms, revealed the importance of parameters such as tissue age and probe design in determining the data quality, and suggested reliable workflows for accurate spatial profiling and molecular discovery.

bioinformatics↗

bioRxiv2024

Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles

Recent developments in immunotherapy, including immune checkpoint blockade (ICB) and adoptive cell therapy, have encountered challenges such as immune-related adverse events and resistance, especially in solid tumors. To advance the field, a deeper understanding of the molecular mechanisms behind treatment responses and resistance is essential. However, the lack of functionally characterized immune-related gene sets has limited data-driven immunological research. To address this gap, we adopted non-negative matrix factorization on 83 human bulk RNA-seq datasets and constructed 28 immune-specific gene sets. After rigorous immunologist-led manual annotations and orthogonal validations across immunological contexts and functional omics data, we demonstrated that these gene sets can be applied to refine pan-cancer immune subtypes, improve ICB response prediction and functionally annotate spatial transcriptomic data. These functional gene sets, informing diverse immune states, will advance our understanding of immunology and cancer research.

bioinformatics↗

bioRxiv2024

Interpretable Spatial Gradient Analysis for Spatial Transcriptomics Data

Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity and can influence tumor invasion, progression, and response to treatment. Here we report Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. To achieve so, LSGI scrutinizes each sliding window employing non-negative matrix factorization (NMF) combined with linear regression. With LSGI, we demonstrated the identification of spatially proximal yet opposite directed pathway gradients in a glioblastoma dataset. We further applied LSGI to 87 tumor ST datasets reported from nine published studies and identified both pan-cancer and tumor-type specific pathways with gradated expression patterns, such as epithelial mesenchymal transition, MHC complex, and hypoxia. The local gradients were further categorized according to their association to tumor-TME (tumor microenvironment) interface, highlighting the pathways related to spatial transcriptional intratumoral heterogeneity. We conclude that LSGI enables highly interpretable STG analysis which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.

bioinformatics↗

bioRxiv2022

Metabolic plasticity of T-cell therapies: multi-omic profiling of interacting human tumor-infiltrating lymphocytes and autologous tumor adoptive cell therapy

Adoptive cell therapy (ACT) based on ex vivo expanded autologous tumor-infiltrating lymphocytes (TILs) can mediate durable antitumor responses even in heavily pretreated patients. However, only a subset of patients responds to ACT; efforts to identify correlates of response have focused on profiling the tumor or the TIL but rarely in an interactive environment. Interactive profiling can provide unique insights into the clinical performance of TILs since the fate, function, and metabolism of TILs are influenced by autologous tumor-derived factors. Here, we performed a suite of cell-sparing assays dubbed holistic analysis of the bioactivity of interacting T cells and autologous tumor cells (HABITAT). HABITAT profiling of TILs used for human ACT and their autologous tumor cells included function-based single-cell profiling by timelapse imaging microscopy in nanowell grids (TIMING); multi-omics using RNA-sequencing and proteomics; metabolite inference using genome-scale metabolic modeling, and pulse-chase assays based on confocal microscopy to profile the uptake and fate of fatty acids (FA). Phenotypically, the ACT TILs from both responders (Rs) and nonresponders (NRs) were comprised of predominantly effector memory T cells (TEM cells) and did not express a high frequency of programmed death ligand-1 (PD-L1) and showed no differences in TCR diversity. Our results demonstrate that while tumor cells from both Rs and NRs are efficient at uptaking FAs, R TILs are significantly more efficient at utilizing FA through fatty acid oxidation (FAO) than NR TILs under nutrient starvation conditions. While it is likely that lipid and FA uptake is an inherent adaptation of TIL populations to lipid-rich environments, performing FAO sustains the survival of TILs and allows them to sustain antitumor cytolytic activity. We propose that metabolic plasticity enabling FAO is a desirable attribute of human TILs for ACT leading to clinical responses.

immunology↗