Biology subjects

Rose, L. A.

Publications and source records attributed to Rose, L. A..

3 recordsLinked to original sources

bioRxiv2025

Spatially distinct chromatin compaction states predict neoadjuvant chemotherapy resistance in Triple Negative Breast Cancer

Organisation and dynamics of chromatin play a key role in regulation of cell state and function. In cancer, chromatin plasticity is known to be important in control of drug resistance, but the relationship between chromatin compaction and chemotherapy response within complex tissue settings remains unclear. Here, we measured single nuclei chromatin compaction using fluorescence lifetime imaging microscopy (FLIM) in situ in whole biopsies from 53 pre treatment and 14 post surgery tissue samples of human triple-negative breast cancer (TNBC) patients to determine whether single nuclei spatial chromatin compaction state can predict resistance to neoadjuvant chemotherapy (NACT). Bulk chromatin compaction across 53 pre-treatment core biopsies did not predict patient outcome. However, machine learning analysis revealed that a subset of patients exhibited distinct distributions of single nuclei with more open chromatin states, which was predictive of NACT-resistance. Graph neural network analysis established that the spatial arrangement of chromatin compaction contributed to prediction of NACT resistance and that chromatin compaction signatures were preserved in tissue state transitions from pre- to 14 post-NACT samples. Our findings shed new light on spatial control of chromatin structure and relationship to therapeutic resistance and establish a foundation for further molecular analysis of chromatin states in complex biological tissues.

systems biology↗

bioRxiv2024

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

Changes in cell shape are fundamentally involved in signaling, intracellular organization, function, and intercellular interactions within tissues, in health and disease. Investigating the interplay between cell shape and protein expression was limited, until recently, by the number of proteins that can be imaged simultaneously or by population averaging. We combined spatial multiplexed single cell imaging and machine learning to systematically investigate the intricate relationships between cell shape and protein expression in the context of heterogeneous human cells in their native state in human tissue samples in situ. Our analysis established a universal bi-directional link between the cells shape and its protein expression across different cell types, diseases, and disease states in human tissues, enabling new applications. Machine learning interpretability showed that the contribution of shape features to a prediction can potentially infer new protein functions. Unbiased screening of the links between all pairs consisting of one protein and one cell type identified a subpopulation of large p53-positive tumor cells across two cancers. Ultimately, inclusion of single cell shape properties enhanced Graph Neural Network disease state prediction. Our results open the door to unraveling the intricate connections between protein expression at the single cell level, cell shape, tissue organization, and tissue state in a physiological context.

cell biology↗

bioRxiv2023

Nondestructive Spatial Lipidomics for Glioma Classification

Mapping the molecular composition of tissues using spatial biology provides high-content information for molecular diagnostics. However, spatial biology approaches require invasive procedures to collect samples and destroy the investigated tissue, limiting the extent of analysis, particularly for highly functional tissues such as those of the brain. To address these limitations, we developed a workflow to harvest biomolecules from brain tissues using nanoneedles and characterise the distribution of lipids using desorption electrospray ionization mass spectrometry imaging. The nanoneedles preserved the original tissue while harvesting a reliable molecular profile and retaining the original lipid distribution for mouse and human brain samples, accurately outlining the morphology of key regions within the brain and tumour lesions. The deep neural network analysis of a cohort containing 23 human glioma biopsies showed that nanoneedle samples maintain the molecular signatures required to accurately classify disease state. Thus, nanoneedles provide a route for tissue-preserving spatial lipidomic and molecular diagnostics.

bioengineering↗