Biology subjects

Brown, E. L.

Publications and source records attributed to Brown, E. L..

7 recordsLinked to original sources

bioRxiv2024

Mapping single-cell developmental potential in health and disease with interpretable deep learning

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cell fate in developmental systems. However, identifying the molecular hallmarks of potency - the capacity of a cell to differentiate into other cell types - has remained challenging. Here, we introduce CytoTRACE 2, an interpretable deep learning framework for characterizing potency and differentiation states on an absolute scale from scRNA-seq data. Across 31 human and mouse scRNA-seq datasets encompassing 28 tissue types, CytoTRACE 2 outperformed existing methods for recovering experimentally determined potency levels and differentiation states covering the entire range of cellular ontogeny. Moreover, it reconstructed the temporal hierarchy of mouse embryogenesis across 62 timepoints; identified pan-tissue expression programs that discriminate major potency levels; and facilitated discovery of cellular phenotypes in cancer linked to survival and immunotherapy resistance. Our results illuminate a fundamental feature of cell biology and provide a broadly applicable platform for delineating single-cell differentiation landscapes in health and disease.

genomics↗

bioRxiv2023

In Vivo Monitoring of Cellular Senescence by Photoacoustic and Fluorescence Imaging Utilizing a Nanostructured Organic Probe

Senescent cells accumulate in multiple age-related disorders, including cancer, exacerbating the pathological manifestations, and the eradication of these cells has emerged as a promising therapeutic strategy. Despite the impact of senescence in diseases, the development of tools to monitor the senescent burden in vivo remains a challenge due to their suboptimal specificity, translatability, and tissue penetrance. Here, we have designed a nanostructured organic probe (NanoJaggs) based on biocompatible indocyanine green dye (ICG) building blocks forming J-aggregates, which possess distinct spectral properties allowing both fluorescence and photoacoustic tomography (PAT) detection. We show that NanoJaggs are taken up by an active process of endocytosis and exhibit selective accumulation at the lysosomal compartment in several in vitro models for senescence. Finally, NanoJagg probe is validated in two in vivo studies including live PAT imaging and shows remarkable specificity to tumours with chemotherapy-induced senescence compared to untreated proliferative tumors. In vitro, ex vivo and in vivo all indicate that NanoJaggs are a clinically translatable tool for detection of senescence and their robust PAT signal makes them suitable for longitudinal monitoring of the senescent burden in solid tumors after chemo or radiotherapy.

cancer biology↗

bioRxiv2023

Segmentation of 3D blood vessel networks using unsupervised deep learning

Mesoscopic photoacoustic imaging (PAI) enables label-free visualisation of vascular networks in tissue at high contrast and resolution. The segmentation of vascular networks from 3D PAI data and interpretation of their meaning in the context of physiological and pathological processes is a crucial but time consuming and error-prone task. Deep learning holds potential to solve these problems, but current supervised analysis frameworks require human-annotated ground-truth labels. Here, we overcome the need for ground-truth labels by introducing an unsupervised image-to-image translation deep learning model called the vessel segmentation generative adversarial network (VAN-GAN). VAN-GAN integrates synthetic blood vessel networks that closely resemble real-life anatomy into its training process and learns to replicate the underlying physics of the PAI system in order to learn how to segment vasculature from 3D biomedical images. With a variety of in silico, in vitro and in vivo data, including patient-derived breast cancer xenograft models, we show that VAN-GAN facilitates accurate and unbiased segmentation of 3D vascular networks from PAI data volumes. By leveraging synthetic data to reduce the reliance on manual labelling, VAN-GAN lowers the barrier to entry for high-quality blood vessel segmentation to benefit users in the life sciences applying PAI to studies of vascular structure and function.

biophysics↗

bioRxiv2022

Performance evaluation of mesoscopic photoacoustic imaging

Photoacoustic mesoscopy visualises vascular architecture and associated tissue structures at high resolution at up to 3 mm depth. The technique has shown promise in both preclinical and clinical imaging studies, with possible applications in oncology and dermatology, however, the accuracy and precision of photoacoustic mesoscopy has not been well established. Here, we present a performance evaluation of a commercial photoacoustic mesoscopy system for imaging vascular structures. Typical artefact types are first highlighted and limitations due to non-isotropic illumination and detection are evaluated with respect to rotation, angularity, and depth of the target. Then, using tailored phantoms and mouse models we demonstrate high system precision, with acceptable coefficients of variation (COV) between repeated scans (short term (1h): COV=1.2%; long term (25 days): COV=9.6%), from target repositioning (without: COV=1.2%, with: COV=4.1%), or from varying in vivo user experience (experienced: COV=15.9%, unexperienced: COV=20.2%). While our findings support the robustness of the technique, they also underscore the general challenges of limited field-of-view photoacoustic systems in accurately imaging vessel-like structures, thereby guiding users to correctly interpret biologically-relevant information.

bioengineering↗

bioRxiv2022

Robust alignment of single-cell and spatial transcriptomes with CytoSPACE

Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, a method for aligning single-cell and spatial transcriptomes via convex linear optimization. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise-tolerance, accuracy, and efficiency, enabling improved analysis of spatial transcriptomics data at single-cell resolution.

genomics↗

bioRxiv2021

Quantification of vascular networks in photoacoustic mesoscopy

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature and has the potential to assess prognosis and therapeutic response. Currently, evaluating vasculature using mesoscopic PAI involves visual or semi-quantitative 2D measurements, which fail to capture 3D vessel network complexity, and lack robust ground truths for assessment of segmentation accuracy. Here, we developed an in silico, phantom, in vivo, and ex vivo-validated end-to-end framework to quantify 3D vascular networks captured using mesoscopic PAI. We applied our framework to evaluate the capacity of rule-based and machine learning-based segmentation methods, with or without vesselness image filtering, to preserve blood volume and network structure by employing topological data analysis. We first assessed segmentation performance against ground truth data of in silico synthetic vasculatures and a photoacoustic string phantom. Our results indicate that learning-based segmentation best preserves vessel diameter and blood volume at depth, while rule-based segmentation with vesselness image filtering accurately preserved network structure in superficial vessels. Next, we applied our framework to breast cancer patient-derived xenografts (PDXs), with corresponding ex vivo immunohistochemistry. We demonstrated that the above segmentation methods can reliably delineate the vasculature of 2 breast PDX models from mesoscopic PA images. Our results underscore the importance of evaluating the choice of segmentation method when applying mesoscopic PAI as a tool to evaluate vascular networks in vivo.

bioengineering↗

bioRxiv2019

Characterization of Peripheral Blood Mononuclear Cells Gene Expression Profiles of Pediatric Staphylococcus aureus Persistent and Non-Carriers Using a Targeted Assay

Defects in innate immunity affect many different physiologic systems and several studies of patients with primary immunodeficiency disorders demonstrated the importance of innate immune system components in disease prevention or colonization of bacterial pathogens. To assess the role of the innate immune system on nasal colonization with Staphylococcus aureus, innate immune responses in pediatric S. aureus nasal persistent carriers (n=15) and non-carriers (n=15) were profiled by analyzing co-clustered gene sets (modules) identified through large-scale transcriptome data analysis as the basis for the development of a targeted assay. We stimulated previously frozen peripheral blood mononuclear cells (PBMCs) from these subjects with i) a panel of TLR ligands, ii) live S. aureus (either a mixture of strains or stimulation with respective carriage isolates), or iii) heat-killed S. aureus. We found no difference in responses between carriers and non-carriers when PBMCs were stimulated with a panel of TLR ligands. However, PBMCs stimulated with live S. aureus elicited a significantly different response that also differed from the response elicited following stimulation with dead S. aureus. Furthermore, we observed a distinct stimulation profile for PBMCs isolated from persistent carriers stimulated with their respective live or dead carriage strains compared to responses observed for PBMCs isolated from non-carriers that were similar regardless of whether or not the bacteria were alive or not. These data suggested that innate pathway signaling is different between persistent and non-carriers of S. aureus.

immunology↗