Biology subjects

Abel, J.

Publications and source records attributed to Abel, J..

5 recordsLinked to original sources

bioRxiv2025

REPTRA: Mapping Immune T Cell Receptor Activity from Full Sequences with a Debiased Contrastive Loss

Contrastive learning is amenable to representing the vast space of interaction between highly specific T cell receptors (TCRs) and the epitopes to which they bind, potentially enabling diverse applications in immune engineering. However, progress in mapping TCR-epitope recognition has been limited by skewed datasets and training approaches that may exacerbate biases and obfuscate model performance. Furthermore, most TCR-epitope models represent only one of three complementaritydetermining regions (CDRs) of the TCR, potentially limiting performance. Here, we present a CLIP-style contrastive-learning model for representation of epitopes and T cell receptor activity (REPTRA), incorporating full TCR sequences and trained using a debiased InfoNCE loss. We trained this model on a dataset with over fivefold more epitope diversity than previous reports, collected using the DECODE platform (Repertoire Immune Medicines). We demonstrate resulting improvements in model performance, and ablate the modified loss and TCR representation to demonstrate the contributions of this approach. Furthermore, we apply an interpretability analysis to the REPTRA attention-pooling projection heads to reveal that CDR1 and CDR2, in addition to CDR3, are important for learning TCR-epitope recognition. In doing so, we develop a performant model for contrastive mapping of T cell receptor activity.

immunology↗

bioRxiv2024

Spatial mapping of immunosuppressive cancer-associated fibroblast gene signatures in H&E-stained images using additive multiple instance learning

The relative abundance of cancer-associated fibroblast (CAF) subtypes influences a tumors response to treatment, especially immunotherapy. However, the extent to which the underlying tumor composition associates with CAF subtype-specific gene expression is unclear. Here, we describe an interpretable machine learning (ML) approach, additive multiple instance learning (aMIL), to predict bulk gene expression signatures from H&E-stained whole slide images (WSI), focusing on an immunosuppressive LRRC15+ CAF-enriched TGF{beta}-CAF signature. aMIL models accurately predicted TGF{beta}-CAF across various cancer types. Tissue regions contributing most highly to slide-level predictions of TGF{beta}-CAF were evaluated by ML models characterizing spatial distributions of diverse cell and tissue types, stromal subtypes, and nuclear morphology. In breast cancer, regions contributing most to TGF{beta}-CAF-high predictions ("excitatory") were localized to cancer stroma with high fibroblast density and mature collagen fibers. Regions contributing most to TGF{beta}-CAF-low predictions ("inhibitory") were localized to cancer epithelium and densely inflamed stroma. Fibroblast and lymphocyte nuclear morphology also differed between excitatory and inhibitory regions. Thus, aMIL enables a data-driven link between tissue phenotype and transcription, offering biological interpretability beyond typical black-box models.

cancer biology↗

bioRxiv2023

Cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer types

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

cancer biology↗

bioRxiv2023

IgG4 serum levels are not elevated in cases of Post-COVID syndrome

Recently, unexpectedly high virus-specific IgG4 levels were reported after more than two mRNA vaccinations. Class switch towards IgG4 occurs after long-term antigen exposure, downregulates immune responses and is associated with several autoimmune diseases. Here, we examined differences in antigen-specific IgG subtypes in serum samples from 64 Post-COVID patients and an equally sized cohort of convalescent controls. In both cohorts, the relative amounts of spike protein-specific IgG subtypes were comparable. IgG1 was the most frequent, followed by IgG3, IgG2, and IgG4. A difference between cohorts was observed only for IgG2, which was significantly lower in the Post-COVID cohort. Further analysis of the reactive IgG4 revealed a small but significant difference for the spike protein receptor-binding domain but not for the spike ectodomain. Since the total IgG4 levels are very low, we do not expect a biologically relevant role in Post-COVID syndrome. However, reduced virus-specific IgG2 levels could contribute to the persistence of SARS-CoV-2, causing chronic inflammation in the setting of Post-COVID syndrome.

immunology↗

bioRxiv2020

Detection of native and mirror protein structures based on Ramachandran plot analysis by interpretable machine learning models

In this contribution the discrimination between native and mirror models of proteins according to their chirality is tackled based on the structural protein information. This information is contained in the Ramachandran plots of the protein models. We provide an approach to classify those plots by means of an interpretable machine learning classifier - the Generalized Matrix Learning Vector Quantizer. Applying this tool, we are able to distinguish with high accuracy between mirror and native structures just evaluating the Ramachandran plots. The classifier model provides additional information regarding the importance of regions, e.g. -helices and {beta}-strands, to discriminate the structures precisely. This importance weighting differs for several considered protein classes.

bioinformatics↗