Biology subjects

Rajpoot, N.

Publications and source records attributed to Rajpoot, N..

7 recordsLinked to original sources

bioRxiv2025

ShapeSpaceExplorer: Analysis of morphological transitions in migrating cells using similarity-based shape space mapping

Here we describe the development of ShapeSpaceExplorer, an interactive software for the extraction, visualisation and analysis of complex 2D shape series. We also demonstrate its application to the analysis of cell morphology changes during cell migration. Cell migration is essential for many physiological and pathological processes. Intracellular force generation and transmission of these forces to extracellular structures or neighbouring cells drives cell migration. The emergent property of the processes driving cell migration is a change in cell shape. We describe a machine learning approach to understand the relationship of cell shape dynamics and cell migration behaviour. Our algorithm analyses cell shape from time-lapse images and learns the intrinsic low-dimensional structure of cell shape space. We use the resultant shape space map to visualise differences in cell shape distribution following perturbation experiments and to analyse the quantitative relationships between shape and migration behaviour. The core of our algorithm is a new, rapid, and landmark-free shape difference measure that allows unbiased analysis of the widely varying morphologies exhibited by migrating mesenchymal cells. We used our method to predict cell turning from dynamic cell shape information. ShapeSpaceExplorer can be applied widely to visualise and analyse cell morphology changes during development, the cell cycle and stress response, but also to the outlines of clusters, tissues and inanimate objects.

cell biology↗

bioRxiv2024

A Deep Learning Framework for Predicting Prognostically Relevant Consensus Molecular Subtypes in HPV-Positive Cervical Squamous Cell Carcinoma from Routine Histology Images

Despite efforts in human papillomavirus (HPV) prevention and screening, cervical cancer remains the fourth most prevalent cancer among women globally. In this study, we propose an end-to-end deep learning framework to investigate histological correlates of the two consensus molecu-lar subtype (CMS) of HPV-positive cervical squamous cell carcinoma (CSCC) patients. Analysing three international CSCC cohorts (n=545 patients), we demonstrate that the genomically determined CMS can be predicted from routine haematoxylin and eosin (H&E)-stained histology slides, with our Digital-CMS scores achieving significant patient stratifications in terms of disease-specific survival (TCGA p=0.0022, Oslo p=0.0495) and disease-free survival (TCGA p=0.0495, Oslo p=0.0282). In addition, our extensive analyses reveal distinct tumour microenvironment (TME) differences between the two CMS subtypes of the CSCC cohorts. Notably, CMS-C1 CSCC subgroup has markedly increased lymphocyte presence, whereas CMS-C2 subgroup has high nuclear pleomor-phism, an elevated neutrophil-to-lymphocyte ratio, and increased neutrophil density. Analysis of representative histological regions reveals higher degree of malignancy in CMS-C2 patients, as-sociated with poor prognosis. This study introduces a potentially clinically advantageous Digital-CMS score derived from digitised WSIs of routine H&E-stained tissue sections, offers new insights into TME differences impacting patient prognosis and potential therapeutic targets, and identifies histological patterns serving as potential surrogate markers of the two CMS subtypes for clinical application.

pathology↗

bioRxiv2024

Buyer Beware: confounding factors and biases abound when predicting omics-based biomarkers from histological images

BackgroundRecent advancements in computational pathology have introduced deep learning methods to predict genomic, transcriptomic and molecular biomarkers from routine histology whole slide images (WSIs) for cancer diagnosis, prognosis, and treatment. However, existing methods often overlook the critical role of co-dependencies among biomarker statuses during training and inference. We hypothesize that this oversight results in models that predict the combined effect of multiple interdependent biomarkers rather than individual statuses independently, akin to attributing the quality of an orchestral symphony to a single instrument, highlighting limitations of current predictors. MethodsUsing large datasets (n = 8,221 patients), we conducted statistical co-dependence testing to demonstrate significant interdependencies among biomarker statuses in training datasets. Following standard protocols, we trained two machine learning models to predict biomarkers from WSIs achieving or matching state-of-the-art predictive performance. We then employed permutation testing and stratification analysis to evaluate their predictive quality based on the principle of conditional independence, i.e., if a model accurately captures the phenotypic influence of a specific biomarker independent of other biomarkers, its performance should remain consistent across subgroups of patients stratified by other biomarkers, aligning with its overall performance on the entire dataset. FindingsOur statistical analysis reveals significant interdependencies among biomarkers, reflecting expected co-occurrence and mutual exclusivity patterns influenced by pathological and biological processes that are consistent across datasets, as well as sampling artefacts that can be different across datasets. Our results indicate that the predictive quality of an image-based predictor for a biomarker is contingent on the status of other biomarkers, revealing that models capture aggregated influences rather than predicting individual statuses independently. For example, mutation predictions are confounded by the overall tumour mutation burden. We also show that, due to the presence of such correlations, deep learning models may not offer significant advantages in predicting certain biomarkers in comparison to simply using pathologist-assigned grades for their prediction. InterpretationWe show that current deep learning models in computational pathology fall short in isolating individual biomarker effects, leading to confounded and less precise predictions. Our findings suggest revisiting model training protocols to recognize and adjust for biomarker interdependencies at all development stages--from problem definition to usage guidelines. This involves selecting diverse datasets to reflect clinical heterogeneity, defining prediction variables or grouping patients based on co-dependencies, designing models to disentangle complex relationships, and stringent stratification testing. Clinically, failure to account for interdependencies may lead to suboptimal decisions, necessitating appropriate usage guidelines for predictive models.

cancer biology↗

bioRxiv2023

Cancer drug sensitivity prediction from routine histology images

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomized controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression based mapping of drug effects on cancer cell lines to train a deep learning model that predicts sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients. HighlightsO_LIPredicting drug sensitivity from routine histology images and cell lines C_LIO_LIDiscovery of histology image patterns linked to drug sensitivity C_LIO_LIA novel deep learning pipeline for analysing drug sensitivity profiles C_LI

cancer biology↗

bioRxiv2023

Data-Driven Modelling of Gene Expression States in Breast Cancer and their Prediction from Routine Whole Slide Images

Identification of gene expression state of a cancer patient from routine pathology imaging and characterization of its phenotypic effects have significant clinical and therapeutic implications. However, prediction of expression of individual genes from whole slide images (WSIs) is challenging due to co-dependent or correlated expression of multiple genes. Here, we use a purely data-driven approach to first identify groups of genes with co-dependent expression and then predict their status from (WSIs) using a bespoke graph neural network. These gene groups allow us to capture the gene expression state of a patient with a small number of binary variables that are biologically meaningful and carry histopathological insights for clinically and therapeutic use cases. Prediction of gene expression state based on these gene groups allows associating histological phenotypes (cellular composition, mitotic counts, grading, etc.) with underlying gene expression patterns and opens avenues for gaining significant biological insights from routine pathology imaging directly. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/536756v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@74d0dcorg.highwire.dtl.DTLVardef@13c2708org.highwire.dtl.DTLVardef@26a6dborg.highwire.dtl.DTLVardef@194b076_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIData-driven discovery of co-expressing gene groups in breast caner C_LIO_LIHistological imaging based prediction of gene groups via deep learning C_LIO_LIIdentification of phenotypic correlates of gene-expression in histological imaging C_LIO_LIClinical and therapeutic impact of gene groups and their visual patterns identified C_LI

pathology↗

bioRxiv2021

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

Computational Pathology (CPath) has seen rapid growth in recent years, driven by advanced deep learning (DL) algorithms. These algorithms typically share the same sequence of steps. However, due to the sheer size and complexity of handling large multi-gigapixel whole-slide images, there is no open-source software library that provides a generic end-to-end API for pathology image analysis using best practices for CPath. Most researchers have designed custom pipelines from the bottom-up, restricting the development of advanced CPath algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make CPath more accessible to new and advanced CPath scientists and pathologists alike. We provide a usable and adaptable library with efficient, cutting-edge and unit-tested tools for data loading, pre-processing, model inference, post-processing and visualization. This enables all kinds of users to easily build upon recent DL developments in the CPath literature. TIAToolbox provides a user-friendly modular API to enable seamless integration of advanced DL algorithms. We show with the help of examples how state-of-the-art DL algorithms can be streamlined using TIAToolbox.

bioengineering↗

bioRxiv2020

Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

One of the challenges in the current COVID-19 crisis is the time and cost of performing tests especially for large-scale population surveillance. Since, the probability of testing positive in large population studies is expected to be small (<15%), therefore, most of the test outcomes will be negative. Here, we propose the use of agglomerative sampling which can prune out multiple negative cases in a single test by intelligently combining samples from different individuals. The proposed scheme builds on the assumption that samples from the population may not be independent of each other. Our simulation results show that the proposed sampling strategy can significantly increase testing capacity under resource constraints: on average, a saving of ~40% tests can be expected assuming a positive test probability of 10% across the given samples. The proposed scheme can also be used in conjunction with heuristic or Machine Learning guided clustering for improving the efficiency of large-scale testing further. The code for generating the simulation results for this work is available here: https://github.com/foxtrotmike/AS.

bioinformatics↗