Restrepo, R. | bioRxiv Science

bioRxiv2026

HoloBio: A Holographic Microscopy Tool for Quantitative Biological Analysis

Holographic imaging in microscopy enables label-free quantitative information of biological specimens and has found applications across a wide range of biomedical studies, from cell morphology to particle dynamics; yet its widespread adoption is often limited by the lack of accessible and standardized analysis software. We present HoloBio, an open-source, Python-based graphical user interface developed to address this issue. This software offers two primary operational modes: a Real-Time mode that enables live processing of holograms at video frame rates, and an Offline mode designed for post-processing previously recorded holograms. HoloBio is compatible with holograms recorded using both lens-based and lensless systems, supporting off-axis architectures in telecentric and non-telecentric configurations, as well as slightly off-axis and in-line optical setups. The software incorporates tools for cell tracking, phase profiling, thickness estimation, and morphological analysis, including cell counting and object area quantification. HoloBio is designed to be accessible for users without coding expertise, offering a reproducible, high-throughput environment tailored for researchers in biology, biophotonics, and biomedical imaging.

developmental biology↗

bioRxiv2023

A 50-gene high-risk profile predictive of COVID-19 and Idiopathic Pulmonary Fibrosis mortality originates from a molecular imbalance in monocyte and T-cell subsets that reverses in survivors with post-COVID-19 Interstitial Lung Disease

BackgroundWe aim to study the source of circulating immune cells expressing a 50-gene signature predictive of COVID-19 and IPF mortality. MethodsWhole blood and Peripheral Blood Mononuclear cells (PBMC) were obtained from 231 subjects with COVID-19, post-COVID-19-ILD, IPF and controls. We measured the 50-gene signature (nCounter, Nanostring), interleukin 6 (IL6), interferon {gamma}-induced protein (IP10), secreted phosphoprotein 1 (SPP1) and transforming growth factor beta (TGF-{beta}) by Luminex. PCR was used to validate COVID-19 endotypes. For single-cell RNA sequencing (scRNA-seq) we used Chromium Controller (10X Genomics). For analysis we used the Scoring Algorithm of Molecular Subphenotypes (SAMS), Cell Ranger, Seurat, Propeller, Kaplan-Meier curves, CoxPH models, Two-way ANOVA, T-test, and Fishers exact. ResultsWe identified three genomic risk profiles based on the 50-gene signature, and a subset of seven genes, associated with low, intermediate, or high-risk of mortality in COVID-19 with significant differences in IL6, IP10, SPP1 and TGF{beta}-1. scRNA-seq identified Monocytic-Myeloid-Derived Suppressive cells (M-MDSCs) expressing CD14+HLA DRlowCD163+ and high levels of the 7-gene signature (7Gene-M-MDSC) in COVID-19. These cells were not observed in post-COVID-19-ILD or IPF. The 43-gene signature was mostly expressed in CD4 T and CD8 T cell subsets. Increased expression of the 43 gene signature was seen in T cell subsets from survivors with post-COVID-19-ILD. The expression of these genes remained low in IPF. ConclusionA 50-gene, high-risk profile in COVID-19 is characterized by a genomic imbalance in monocyte and T-cell subsets that reverses in survivors with post-COVID-19 Interstitial Lung Disease

genomics↗