Biology subjects

Rajwa, B.

Publications and source records attributed to Rajwa, B..

5 recordsLinked to original sources

bioRxiv2025

An optimal transport-based low-dimensional visualization framework for high-parameter flow cytometry

Conventional data visualization techniques in single-cell analysis (such as two-dimensional dot plots, SPADE, PCA, t-SNE, or UMAP) often fall short in providing an intuitive understanding of high-parameter flow cytometry data. These methods tend to oversimplify complex biological relationships, lack biologically meaningful interpretations, and offer no principled framework for downstream quantitative analysis. To address these limitations, we present a graph-based visualization framework grounded in optimal transport theory. In this framework, cell populations are defined by their marker-expression profiles, and inter-population similarity is quantified using an efficiently computable optimal transport formulation known as the Sinkhorn distance. Our approach produces biologically consistent two-dimensional graph layouts using a phenotypeaware Hamming distance. Structural differences between sample graphs are characterized through a customized graph-edit distance that captures changes in population size, marker expression, and relationships between populations. We demonstrate our methods on two flow cytometry datasets: one from a clinical trial of dendritic cell-based immunotherapy in malignant peritoneal mesothelioma, involving 14 patients sampled at three time points with 14-color panels, and another from FlowCAP-II, which involved 43 acute myeloid leukemia patient samples analyzed with 7-color panels. Our framework produces robust, quantitative visual summaries of cell populations and supports statistical analysis based on graph edit distances, thereby offering new insights into disease progression and treatment response. Ultimately, our method bridges the gap between flow cytometry data visualization and biological interpretation.

bioinformatics↗

bioRxiv2024

Measuring autofluorescence spectral signatures for detecting antibiotic-resistant bacteria using Bigfoot spectral flow cytometer

Application of flow cytometry to microbiology has been limited due to inadequate availability of bacterial-specific stains, expensive antibody-based fluorophores, ineffective stain cell permeability and challenges in differentiating bacterial cells from cell debris due to their similarity and their small size. In addition, staining cells demands multiple washing steps which limits the sensitivity of detection due to the cell volume that is up to two orders smaller than typical eukaryotic cells. Further, most flow cytometers are not equipped to handle pathogenic organisms. Autofluorescence-based detection of cells can be a useful method for bacterial detection as multiple washing steps can be avoided and it also reduces the time and cost of using stains. Multiple studies have shown that the autofluorescence in bacterial cells are mainly linked to specific proteins, enzymes, or enzyme cofactors such as Flavin Adenine Dinucleotide (FAD) and Nicotinamide Adenine Dinucleotide (NAD) which are involved in bacterial metabolism. In this report, we present a novel method for differentiation between clinically isolated antibiotic resistant and non-resistant bacteria by utilizing their autofluorescence spectral signatures. We utilized a spectral cytometer known as Bigfoot which is equipped with an integrated biosafety cabinet allowing easy handling of pathogenic organisms unlike any other flow cytometers. Bigfoot also has 9 lasers and 54 fluorescence detectors which we utilize to capture the bacterial spectral autofluorescence signatures. As a proof of principle, we initially stressed different types of bacteria (E.coli and Salmonella sp.) using gentamicin antibiotics by collecting spectral autofluorescence over different time points. The spectral signatures were compared with the non-stressed bacteria. We observed that the stressed bacteria showed an increase in autofluorescence at distinct excitation (Ultraviolet, Violet, and blue color) and emission wavelengths whereas the non-stressed did not. The same experiments were repeated to compare the autofluorescence signatures between Methicillin-resistant and methicillin susceptible staphylococcus aureus (MRSA and MSSA) which were stressed with oxacillin antibiotics. MSSA showed an increase in autofluorescence between 4 - 6 h after exposure to oxacillin. MRSA on the other hand showed no increase in autofluorescence and the autofluorescence between stressed and non-stressed MRSA had similar signatures. This demonstrated that the antibiotic resistant or susceptible strain can be detected by observing the change in autofluorescence signature at specific wavelengths in a few hours. This label-free, quantitative, and resistant-specific autofluorescence spectral signatures from the Bigfoot spectral flow cytometer could potentially be utilized for rapid detection of antibioticresistant strain.

bioengineering↗

bioRxiv2024

A versatile information retrieval framework for evaluating profile strength and similarity

In profiling assays, thousands of biological properties are measured across many samples, yielding biological discoveries by capturing the state of a cell population, often at the single-cell level. However, for profiling datasets, it has been challenging to evaluate the phenotypic activity of a sample and the phenotypic consistency among samples, due to profiles high dimensionality, heterogeneous nature, and non-linear properties. Existing methods leave researchers uncertain where to draw boundaries between meaningful biological response and technical noise. Here, we developed a statistical framework that uses the well-established mean average precision (mAP) as a single, data-driven metric to bridge this gap. We validated the mAP framework against established metrics through simulations and real-world data applications, revealing its ability to capture subtle and meaningful biological differences in cell state. Specifically, we used mAP to assess both phenotypic activity for a given perturbation (or a sample) as well as consistency within groups of perturbations (or samples) across diverse high-dimensional datasets. We evaluated the framework on different profile types (image, protein, and mRNA profiles), perturbation types (CRISPR gene editing, gene overexpression, and small molecules), and profile resolutions (single-cell and bulk). Our open-source software allows this framework to be applied to identify interesting biological phenomena and promising therapeutics from large-scale profiling data.

bioinformatics↗

bioRxiv2020

Maternal schistosomiasis impairs offspring IL-4 production and B cell expansion

Maternal helminth infections are a global public health concern and correlate with altered infant immune responses to some childhood immunizations, but a mechanistic understanding of how maternal helminth infection alters the cellular immune responses of offspring is lacking. Here we establish a model of maternal Schistosoma mansoni infection in dual IL-4 reporter mice. We find that offspring born to mothers infected with S. mansoni have impaired production of IL-4 during homoeostasis, and following immunization with a Tetanus-Diphtheria vaccine. We identified that iNKT cells are the dominant source of IL-4 during early life homeostasis, and that diminished IL-4 production was associated with both reduced B cell and follicular dendritic cell responses. These defects were maintained long-term, affecting memory B and T cell responses. Single-cell RNASeq analysis of immunized offspring identified egg antigen-dependent reductions in B-cell cell cycle and proliferation-related genes. These data reveal that maternal infection leads to long-lasting defects in the cellular responses to heterologous antigens and provide vital insight into the influence of maternal infection on offspring immunity.

immunology↗

bioRxiv2020

Schistosoma mansoni infection induces long-lived sex-specific metabolic reprogramming of the myeloid lineage

Despite evidence that helminths protect from metabolic disease, a major gap exists in understanding the underlying mechanism(s). Here we demonstrate that bone marrow derived macrophages (BMDM) from S. mansoni infected male ApoE-/- mice have dramatically increased mitochondrial respiration compared to those from uninfected mice. This change associates with increased glucose and palmitate shuttling into TCA cycle intermediates and decreased accumulation of cellular cholesterol esters. Moreover, systemic metabolic modulation by schistosomes is a function of biological sex, where infection protects ApoE-/- male, but not female, mice from obesity and glucose intolerance. Sex-dependence extends to myeloid cells, where reprogramming leads to opposite cholesterol phenotypes in BMDM from females and males. Finally, the metabolic reprogramming of male myeloid cells is transferrable via bone marrow transplantation to an uninfected host, indicating maintenance of reprogramming in the absence of sustained antigen exposure. This work reveals that S. mansoni systemic reprograming of myeloid metabolism is sex-dependent.

immunology↗