Biology subjects

Chou, P.

Publications and source records attributed to Chou, P..

3 recordsLinked to original sources

bioRxiv2026

Generalizable virtual staining from histopathology to spatial proteomics across biological scales

Large histology archives capture morphological correlates of molecular tissue states but lack direct spatial protein measurements. In this study, we show that routine hematoxylin and eosin (H&E) histology can provide a queryable, morphology-based representation of spatial protein expression that remains informative from pixels to patients. To achieve this, we developed QUEST, a model framework that represents protein markers as semantic queries for flexible multiplex immunofluorescence (MIF) prediction. Trained on a heterogeneous atlas of paired H&E and MIF images comprising over 10,000 tissue regions, QUEST captured morphological patterns associated with protein markers and reconstructed their expression intensities across diverse tissue types and cohorts. Its utility extended beyond image reconstruction and preserved biologically meaningful information across multiple scales: virtual staining preserved protein marker expression at the cellular and tissue scales, supported cell phenotyping, cellular neighborhood analysis, and characterization of spatial tissue organization, retained clinically relevant signals for patient stratification, and enabled H&E-based queries to retrieve MIF references with similar molecular profiles. These findings position virtual staining not simply as image generation but as a flexible interface between routine histology and spatial proteomics, expanding the analytical value of existing H&E archives.

bioinformatics↗

bioRxiv2025

Modeling patient tissues at molecular resolution with Eva

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of over 40 million matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference from H&E to proteomics stains, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.

bioinformatics↗

bioRxiv2020

Development of genetic tools for the thermophilic filamentous fungus Thermoascus aurantiacus

BackgroundFungal enzymes are vital for industrial biotechnology, including the conversion of plant biomass to biofuels and bio-based chemicals. In recent years, there is increasing interest in using enzymes from thermophilic fungi, which often have higher reaction rates and thermal tolerance compared to currently used fungal enzymes. The thermophilic filamentous fungus Thermoascus aurantiacus produces large amounts of highly thermostable plant cell wall degrading enzymes. However, no genetic tools have yet been developed for this fungus, which prevents strain engineering efforts. The goal of this study was to develop strain engineering tools such as a transformation system, a CRISPR/Cas9 gene editing system and a sexual crossing protocol to improve enzyme production. ResultsHere we report Agrobacterium tumefaciens-mediated transformation (ATMT) of T. aurantiacus using the hph marker gene, conferring resistance to hygromycin B. The newly developed transformation protocol was optimized and used to integrate an expression cassette of the transcriptional xylanase regulator xlnR, which led to up to 500% increased xylanase activity. Furthermore, a CRISPR/Cas9 gene editing system was established in this fungus, and two different gRNAs were tested to delete the pyrG orthologue with 10% and 35% deletion efficiency, respectively. Lastly, a sexual crossing protocol was established using a hygromycin B- and a 5-fluororotic acid-resistant parent strain. Crossing and isolation of progeny on selective media was completed in a week. ConclusionThe genetic tools developed for T. aurantiacus can now be used individually or in combination to further improve thermostable enzyme production by this fungus.

microbiology↗