Biology subjects

Mallick, P.

Publications and source records attributed to Mallick, P..

12 recordsLinked to original sources

bioRxiv2026

Multi-Scale Tri-Modal Histology Dataset Integrating Tumor Morphology, Immune Patterns, and Clinical Outcomes

Accurate prognostic assessment of prostate cancer (PCa) requires an integrated understanding of tissue morphology-encompassing cell structure, glandular architecture, and tissue organization-and the immune environment. We present Prostate-TriMod, a novel tri-modal histology dataset designed to integrate high-resolution visual morphology with spatial tissue maps, immune infiltration patterns, and clinical outcomes. This dataset, generated from the Cell DIVE multiplexed imaging platform, consists of three synchronized modalities: (1) multiscale virtual H&E tiles (224px, 256px, 512px, and 2040px) providing visual morphological context, (2) spatial tissue maps identifying cancerous/non-cancerous epithelial cells, stroma and immune cell populations (via TOPAZ and CAT models), and (3) text captions generated from single-cell data and patterns. The dataset includes comprehensive clinical annotations, including Grade Groups and biochemical recurrence (BCR) status. By providing high-fidelity alignment between visual features, spatial tissue maps, and textual descriptions, Prostate-TriMod empowers the development of advanced multimodal AI frameworks. We expect this resource to support reuse in multimodal representation learning, spatial analysis, and benchmarking studies that link histology morphology and immune context to clinical outcomes in prostate cancer.

bioinformatics↗

bioRxiv2025

Annotation-Free Prediction of Cancer Cells and Glands and Spatial Analysis of Immune Cells.

Prostate cancer is classified as "immune-cold" due to limited infiltration of immune cells and no clear correlation between immune cells and clinical outcomes. However, immune cells are found in prostate cancers and the spatial relationships between these immune cells and cancer cells/glands have not been investigated, partly due to a lack of automated tools that classify both cancerous cells/glands. In this paper, we have developed an end-to-end tool (TOPAZ: Tissue Organization identification using sPAtial proteomics) that combines multiplexed single-cell protein data with histology images to: 1) predict cancerous versus non-cancerous epithelial cells using a Gaussian-mixture model; 2) predict cancerous/non-cancerous gland type using a principal curve estimation. Using TOPAZ to assign cancer and non-cancerous labels to cells and glands, we extracted multiscale spatial features from the classification results--including immune dense-region geometrical features and cell-to-gland distances-- and correlated the features with risk of biochemical recurrence and cancer grade. Tissue-microarrays containing 753 cores from 217 prostate cancer patients underwent multiplexed immunofluorescent imaging (Cell DIVE, Leica) for epithelial cell markers (panCK26, S6, NaKATPase), basal cell markers (p63, CK5), a cancer cell marker (AMACR), and T cell markers (CD3, CD4, CD8, FOXP3, CD68). Cancerous/non-cancerous cell classification from TOPAZ achieved 82% sensitivity and 99% specificity against expert annotation, and the pipeline further predicted cancerous/non-cancerous glands without manual threshold tuning. Regulatory-T-cell and helper-T-cell percentages decreased, and macrophage percentage increased with grade increase (P < 0.05). When the median distance from cancerous gland centroids to the nearest regulatory or helper T-cell exceeded approximately 50 {micro}m, the hazard of biochemical recurrence doubled (log-rank P < 0.01). The open-source Shiny app TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) packages the workflow, predicting individual cell types and gland shapes. By combining probabilistic cell typing with gland-shape modeling, TOPAZ yields interpretable multiscale spatial features linked to prognosis and is released as an open web app for unrestricted use. Author SummarySpatial distribution of cancerous cells/glands and immune cell distribution has not been considered as a prognosticator in prostate cancer. In addition, automated tools that can quantify and integrate these distributions are lacking. We combined high-dimensional single-cell protein measurements with histology images to map gland structure in prostate cancer tissue. Our web-based tool, TOPAZ (https://chunglab.bmi.osumc.edu/TOPAZ) predicts whether each epithelial cell and gland in virtual H&E image is cancerous or not. Once the predictions are made, a spatial analysis workflow helps quantify spatial features of immune cells relative to the glands and correlate with recurrence risk and grades. Across 753 tissue cores from 217 prostate cancer patients, helper and regulatory-T-cells located more than about 50 {micro}m away from cancerous epithelial glands were associated with a higher risk of biochemical recurrence. The pipeline provides new insights for researchers and pathologists into prostate cancer progression and biochemical recurrence through integration of spatial location of cancer glands and immune cells.

cancer biology↗

bioRxiv2025

Assessing climate change impacts for small-scale fisheries in the Gulf of California using Deep Learning

Small-scale, multispecific fisheries in the Gulf of California face significant challenges including limited species-specific catch data, uncertainty about climate change impacts, and insufficient biological information needed for traditional deterministic models. These knowledge gaps hamper efforts to forecast future conditions and develop appropriate management strategies accurately. The complexity of these multi-species fisheries, combined with data scarcity for many target species, creates substantial barriers to quantifying and addressing climate vulnerability. Deep learning approaches offer a promising alternative by leveraging available data to identify patterns and project trends despite these limitations, providing valuable insights for fisheries management in data-poor contexts. Here, we apply a Mixture of Expert, a deep learning forecasting models for small-scale, multi-specific fisheries in the Gulf of California under future climate change scenarios. Results show varied responses across marine habitats, with reef and benthic fish projected to experience substantial declines (-12.46% and -9.37%) during the 2050s-2060s, followed by recovery in the 2070s-2080s. Economic implications are significant, with reef fish facing projected losses of $1.2 million by the 2050s before recovering by the 2080s. Shapley Additive Explanations (SHAP) analysis was applied to evaluate the importance of features for each predictive model, the analysis revealed the effects of the temperature in different depths for each fishery, and the sensitive analysis pointed to the magnitude of the effect. Our findings suggest that climate impacts will not be uniform across the Gulf, necessitating region-specific management approaches and highlighting the value of maintaining diverse fishing portfolios to enhance resilience against climate-driven changes.

ecology↗

bioRxiv2024

The impact of ERUPR on mitochondrial integrity mediated by PDK4

ER and mitochondrial stress are often interconnected and considered as major contributors to aging as well as neurodegeneration. Co-ordinated induction of ERUPR and mitoUPR has been observed in diabetes and pulmonary disorders. However, in the context of aging and neurodegeneration, regulation of this intra-organellar crosstalk has remained relatively elusive. Here, we demonstrate that pyruvate dehydrogenase kinase 4 (PDK4), a mitochondrial protein accumulates at the ER-mitochondrial contact sites (MAMs) during ER stress. Classically, PDK4 is known to phosphorylate PDHA1 (pyruvate dehydrogenase E1 subunit alpha 1), and plays a significant role in regulating the oxidative phosphorylation driven ATP production. In this study, we propose a non-canonical function of PDK4; we show that it acts as a connecting link between ERUPR and mitoUPR with significance in aging and neurodegeneration. Transcriptomics analyses show increased PDK4 levels upon drug induced ER stress. We detect elevated PDK4 levels in brain lysates of Alzheimers disease (AD) patients, as well as in in vivo and ex vivo AD models. Additionally, exogenous expression of PDK4 was found to refine ER-mitochondrial communication, significantly altering mitochondrial morphology and function. Further, we also observe defective autophagic clearance of mitochondria under such conditions. It is prudent to suggest that elevated PDK4 levels could be one of the key factors connecting ERUPR with mitoUPR, a phenotypic contributor in aging and at least some neurodegenerative diseases.

molecular biology↗

bioRxiv2024

Examination of the role of mutualism in immune evasion

Though the earliest stages of oncogenesis, post initiation, are not well understood, it is generally appreciated that a successful transition from a collection of dysregulated cells to an aggressive tumor requires complex ecological interactions between cancer cells and their environment. One key component of tumorigenesis is immune evasion. To investigate the interplay amongst the ecological behaviour of mutualism and immune evasion, we used a computational simulation framework. Sensitivity analyses of the growth of a virtual tumor implemented as a 2D-hexagonal lattice model suggests tumor survival depends on the interplay between growth rates, mutualism and immune evasion. In 60% of simulations, cancer clones with low growth rates, but exhibiting mutualism were able to evade the immune system and continue progressing suggesting that tumors with equivalent growth rates and no mutualism are more likely to be eliminated than tumors with mutualism. Tumors with faster growth rates showed a lower dependence upon mutualism for progression. Geostatistical analysis showed decreased spatial heterogeneity over time for polyclonal tumours with a high division rate. Overall, these results suggest that in slow growing tumors, mutualism is critical for early tumorigenesis.

bioinformatics↗

bioRxiv2024

The temporal evolution of cancer hallmarks

Cancer hallmarks describe key physiological characteristics that distinguish cancers from normal tissues. The temporal order in which these hallmarks appear during cancer pathogenesis is of interest from both evolutionary and clinical perspectives but has not been investigated before. Here, we order hallmarks based on the allele frequency and selective advantage of mutations in cancer hallmark genes across >10k untreated primary tumors and >8K healthy tissues. Using this novel approach, we identified a common evolutionary trajectory for 27 of 32 cancer types with genomic instability as the first and immune evasion as the last hallmark. We demonstrated widespread positive selection in cancer and strong negative selection in normal tissues for all hallmarks. Notable exceptions to the hallmark ordering in tumours were melanomas (uveal and skin) suggesting that strong environmental factors could disrupt common evolutionary paths. Clustering of hallmark trajectories across patients revealed 2 clusters defined by early or late genomic instability, with differential prognosis. Our study is the first to identify the temporal order of cancer hallmarks during tumorigenesis and demonstrate a prognostic value that could be exploited for early detection and risk stratification across multiple cancer types.

cancer biology↗

bioRxiv2022

Highly dense and scalable protein arrays for single-molecule studies

Single-molecule proteomic studies are critically important for understanding the molecular origins of cellular phenotypes. However, no currently available technology can achieve both the single-molecule sensitivity and high dynamic range required to comprehensively analyze the complex mixtures of proteins in biological samples. One approach to achieve high sensitivity across a wide dynamic range would be to create a protein array that arranges billions of single molecules with regular spacing on a patterned surface. However, creating such a protein array has remained an unsolved challenge for the field. Here, we present a highly scalable method for fabricating dense single-molecule protein arrays using a specially designed DNA origami structure, protein click-conjugation, photolithography and surface functionalization. The origami-structure is enhanced via terminal deoxynucleotidyl transferase-extension, which generates brush-like projections, increasing the effective size of the origami from 88 nm to greater than 200 nm. These particles are large enough to enable super-Poisson deposition of individual protein molecules on a nano-patterned chip (>98% occupancy with only 1% of sites occupied with multiple protein molecules). This approach allowed for single-molecule protein display of 600 million protein molecules per microscope slide-sized chip with the potential to scale further with denser feature spacing. We hypothesize that this technology will ultimately enable the development of highly scalable proteomic analysis platforms that address the currently unmet need for protein measurements at single-molecule sensitivity across an exceptionally wide dynamic range of protein concentrations.

bioengineering↗

bioRxiv2021

A theoretical framework for proteome-scale single-molecule protein identification using multi-affinity protein binding reagents

The proteome is perhaps the most dynamic and valuable source of functional biological insight. Current proteomic techniques are limited in their sensitivity and throughput. A typical single experiment measures no more than 8% of the human proteome from blood or 35% from cells and tissues 1, 2. Here, we introduce a theoretical framework for a fundamentally different approach to proteomics that we call Protein Identification by Short-epitope Mapping (PrISM). PrISM utilizes multi-affinity reagents to target short linear epitopes with both a high affinity and low specificity. PrISM further employs a novel protein decoding algorithm that considers the stochasticity expected for single-molecule binding. In simulations, PrISM is able to identify more than 98% of proteins across the proteomes of a wide range of organisms. PrISM is robust to potential experimental confounders including false negative detection events and noise. Simulations of the approach with a chip containing 10 billion protein molecules show a dynamic range of 11.5 and 9.5 orders of magnitude for blood plasma and HeLa cells, respectively. If implemented experimentally, PrISM stands to rapidly quantify over 90% of the human proteome in a single experiment, potentially revolutionizing proteomics research.

systems biology↗

bioRxiv2021

A flexible electronic strain sensor for the real-time monitoring of tumor progression

Healthcare professionals and scientists utilize tumor shrinkage as a key metric to establish the efficacy of cancer treatments. However, current measurement tools such as CT scanners and calipers only provide brief snapshots of the dynamic geometric changes occurring in vivo, and they are unable to characterize the continuous micrometer-scale volumetric transformations transpiring at minute timescales. Here we present a stretchable electronic strain sensor, with a 10-micron scale resolution, capable of continuously monitoring tumor volume progression in real-time. In mouse models with subcutaneously implanted lung cancer or B-cell lymphoma tumors our sensors discerned a significant change in the tumor volumes of treated mice within 5 hours after small molecule therapy or immunotherapy initiation. Histology, caliper measurements, and luminescence imaging over a one-week treatment period validated the data from the continuous sensor. We anticipate that real-time tumor progression datasets could help expedite and automate the process of screening cancer therapies in vivo.

bioengineering↗

bioRxiv2021

Modular Fluorescent Nanoparticle DNA Probes for Detection of Peptides and Proteins

Fluorescently labeled antibody and aptamer probes are used in biological studies to characterize binding interactions, measure concentrations of analytes, and sort cells. Fluorescent nanoparticle labels offer an excellent alternative to standard fluorescent labeling strategies due to their enhanced brightness, stability and multivalency; however, challenges in functionalization and characterization have impeded their use. This work introduces a straightforward approach for preparation of fluorescent nanoparticle probes using commercially available reagents and common laboratory equipment. Fluorescent polystyrene nanoparticles, Thermo Fisher Scientific FluoSpheres, were used in proof-of-principle studies. Particle passivation was achieved by covalent attachment of amine-PEG-azide to carboxylated particles, neutralizing the surface charge from -43 to -15 mV. A conjugation-annealing handle and DNA aptamer probe was attached to the azide-PEG nanoparticle surface either through reaction of pre-annealed handle and probe or through a stepwise reaction of the nanoparticles with the handle followed by aptamer annealing. Nanoparticles functionalized with DNA aptamers targeting histidine tags and VEGF protein had high affinity (EC50s ranging from 3-12 nM) and specificity, and were more stable than conventional labels. This protocol for preparation of nanoparticle probes relies solely on commercially available reagents and common equipment, breaking down the barriers to use nanoparticles in biological experiments.

bioengineering↗

bioRxiv2021

Genomic surveillance and phylodynamic analyses reveal emergence of novel mutation and co-mutation patterns within SARS-CoV2 variants prevalent in India

Emergence of distinct viral clades has been observed in SARS-CoV2 variants across the world and India. Identification of the genomic diversity and the phylodynamic profiles of the prevalent strains of the country are critical to understand the evolution and spread of the variants. We performed whole-genome sequencing of 54 SARS-CoV2 strains collected from COVID-19 patients in Kolkata, West Bengal during August to October 2020. Phylogeographic and phylodynamic analyses were performed using these 54 and other sequences from India and abroad available in GISAID database. Spatio-temporal evolutionary dynamics of the pathogen across various regions and states of India over three different time periods in the year 2020 were analyzed. We estimated the clade dynamics of the Indian strains and compared the clade specific mutations and the co-mutation patterns across states and union territories of India over the time course. We observed that GR, GH and G (GISAID) or 20B and 20A (Nextstrain) clades were the prevalent clades in India during middle and later half of the year 2020. However, frequent mutations and co-mutations observed within the major clades across time periods do not show much overlap, indicating emergence of newer mutations in the viral population prevailing in the country. Further, we explored the possible association of specific mutations and co-mutations with the infection outcomes manifested within the Indian patients.

genomics↗

bioRxiv2019

Geostatistical visualization of ecological interactions in tumors

Recent advances in our understanding sof cancer progression have highlighted the important role played by proteomic heterogeneity and the tumor microenvironment. Single-cell measurement technologies have enabled deep investigation of tumor heterogeneity. However, tools to visualize and interpret single-cell data have lagged behind experimental methods; currently, dimensionality reduction and clustering techniques, such as t-sne and SPADE, are the most prevalent visualization techniques. However, such techniques do not enable the visualization of the super-cellular structures that arise either via microenvironmental forces (e.g. hypoxia) or through cell-cell interactions. They additionally do not allow the visualization of ecological niches that are critical to understanding tumor behavior. The advent of novel experimental hyperplex immunostaining platforms, capable of measuring the in situ protein expression of dozens of proteins simultaneously, necessitates novel computational tools to quantify and visualize spatial patterns in the tumor microenvironment. Here, we introduce an approach to visualize tumor heterogeneity that integrates multiple geostatistics to capture both global and local spatial patterns. We assess the utility of this approach using an agent-based model of tumor growth under four ecological contexts; predation, mutualism, commensalism, and parasitism. Spatial patterns are visualized in real time as the models progress. This application introduces both an ecological framework for characterizing cellular interactions in cancer and novel way of quantifying and visualizing spatial patterns in cancer.

bioinformatics↗