Biology subjects

Ibarra-Arellano, M. A.

Publications and source records attributed to Ibarra-Arellano, M. A..

4 recordsLinked to original sources

bioRxiv2025

Comparison and Optimization of Cellular Neighbor Preference Methods for Quantitative Tissue Analysis

Studying the spatial distribution of cell types in tissues is essential for understanding their function in health and disease. A widely used spatial feature for quantifying tissue organization is the pairwise neighbor preference (NEP) of cell types, commonly referred to as co-occurrence or colocalization. Various methods to infer NEP have proved their utility in spatial omics studies, but despite their broad usage, no clear guidelines exist for selecting one method over the other. In this paper, we deconstruct frequently used methods into their underlying analysis steps and evaluate their optimal combination. We studied the methods on two aspects: (1) their discriminatory power to distinguish different tissue architectures and (2) their ability to recover the directionality of NEPs. We compared existing as well as our in-house developed method (conditional z-score (COZI)) and compared their performance using in silico tissue simulations and demonstrated its biological applicability in a myocardial infarction dataset. Overall, our study serves as a comprehensive guide for users and method developers in spatial omics analysis and offers a novel approach (COZI), which outperforms existing methods, to performing NEP analysis.

bioinformatics↗

bioRxiv2024

micronuclAI: Automated quantification of micronuclei for assessment of chromosomal instability.

Chromosomal instability (CIN) is a hallmark of cancer that drives metastasis, immune evasion and treatment resistance. CIN results from chromosome mis-segregation events during anaphase, as excessive chromatin is packaged in micronuclei (MN), that can be enumerated to quantify CIN. Despite recent advancements in automation through computer vision and machine learning, the assessment of CIN remains a predominantly manual and time-consuming task, thus hampering important work in the field. Here, we present micronuclAI, a novel pipeline for automated and reliable quantification of MN of varying size, morphology and location from DNA-only stained images. In micronucleAI, single-cell crops are extracted from high-resolution microscopy images with the help of segmentation masks, which are then used to train a convolutional neural network (CNN) to output the number of MN associated with each cell. The pipeline was evaluated against manual single-cell level counts by experts and against routinely used MN ratio within the complete image. The classifier was able to achieve a weighted F1 score of 0.937 on the test dataset and the complete pipeline can achieve close to human-level performance on various datasets derived from multiple human and murine cancer cell lines. The pipeline achieved a root-mean-square deviation (RMSE) value of 0.0041, an R2 of 0.87 and a Pearsons correlation of 0.938 on images obtained at 10X magnification. We tested the approach in otherwise isogenic cell lines in which we genetically dialed up or down CIN rates, and also on a publicly available image data set (obtained at 100X) and achieved an RMSE value of 0.0159, an R2 of 0.90, and a Pearsons correlation of 0.951. Given the increasing interest in developing therapies for CIN-driven cancers, this method provides an important, scalable, and rapid approach to quantifying CIN on routinely obtained images. We release a GUI-implementation for easy access and utilization of the pipeline.

systems biology↗

bioRxiv2024

Spatial omics of acute myocardial infarction reveals a novel mode of immune cell infiltration

Myocardial infarction (MI) continues to be a leading cause of death worldwide. Even though it is well-established that the complex interplay between different cell types determines the overall healing response after MI, the precise changes in the tissue architecture are still poorly understood. Here we generated an integrative cellular map of the acute phase after murine MI using a combination of imaging-based transcriptomics (Molecular Cartography) and antibody-based highly multiplexed imaging (Sequential Immunofluorescence), which enabled us to evaluate cell-type compositions and changes at subcellular resolution over time. One striking finding of these analyses was the identification of a novel mode of leukocyte accumulation to the infarcted heart via the endocardium - the inner layer of the heart. To investigate the underlying mechanisms driving this previously unknown infiltration route, we performed unbiased spatial proteomic analysis using Deep Visual Proteomics (DVP). When comparing endocardial cells of homeostatic hearts and infarcted hearts, DVP identified von Willebrand Factor (vWF) as an upregulated mediator of inflammation 24 hours post-MI. To further explore the immune mediating capabilities of vWF and its effect on tissue repair, we performed functional blocking of vWF during acute murine MI. This resulted in a reduced amount of infiltration by CCR2+ monocytes and worse cardiac function post-MI. Our study provides the first spatial map of acute murine MI with subcellular resolution and subsequently discovers a novel route of immune infiltration. Furthermore, we identified vWF as a critical immune mediating agent for endocardial immune cell infiltration.

systems biology↗

bioRxiv2022

Spatial cell type mapping of multiple sclerosis lesions

Multiple sclerosis (MS) is a prototypic chronic-inflammatory disease of the central nervous system. After initial lesion formation during active demyelination, inflammation is gradually compartmentalized and restricted to specific tissue areas such as the lesion rim in chronic-active lesions. However, the cell type-specific and spatially restricted drivers of chronic tissue damage and lesion expansion are not well understood. Here, we investigated the properties of subcortical white matter lesions by creating a cell type-specific spatial map of gene expression across various inflammatory lesion stages in MS. An integrated analysis of single-nucleus and spatial transcriptomics data enabled us to uncover patterns of glial, immune and stromal cell subtype diversity, as well as to identify cell-cell communication and signaling signatures across lesion and non-lesion tissue areas in MS. Our results provide insights into the conversion of the tissue microenvironment from a homeostatic to a pathogenic or dysfunctional state underlying lesion progression in MS. We expect that this study will help identify spatially resolved cell type-specific biomarkers and therapeutic targets for future interventional trials in MS.

neuroscience↗