Biology subjects

Rocke, D. M.

Publications and source records attributed to Rocke, D. M..

2 recordsLinked to original sources

bioRxiv2022

Convection and extracellular matrix binding control interstitial transport of extracellular vesicles

Extracellular vesicles (EVs) influence a host of normal and pathophysiological processes in vivo. Compared to soluble mediators, EVs are relatively large (~30-150 nm) and can traffic a wide range of proteins on their surface including extracellular matrix (ECM) binding proteins. We isolated EVs from the MCF10 series - a model human cell line of breast cancer progression - and demonstrated increasing presence of laminin-binding integrins 3{beta}1 and 6{beta}1 on the EVs as the malignant potential of the MCF10 cells increased. Transport of the EVs within a microfluidic device under controlled physiological interstitial flow (0.15-0.75 m/s) demonstrated that convection was the dominant mechanism of transport. Binding of the EVs to the ECM enhanced the spatial concentration and gradient, which was partially mitigated by blocking integrins 3{beta}1 and 6{beta}1. Our studies demonstrate that convection and ECM binding are the dominant mechanisms controlling EV interstitial transport and should be leveraged in the design of nanotherapeutics.

bioengineering↗

bioRxiv2020

Deep Semi-Supervised Learning Improves Universal Peptide Identification of Shotgun Proteomics Data

Semi-supervised machine learning post-processors critically improve peptide identification of shot-gun proteomics data. Such post-processors accept the peptide-spectrum matches (PSMs) and feature vectors resulting from a database search, train a machine learning classifier, and recalibrate PSMs using the trained parameters, often yielding significantly more identified peptides across q-value thresholds. However, current state-of-the-art post-processors rely on shallow machine learning methods, such as support vector machines. In contrast, the powerful training capabilities of deep learning models have displayed superior performance to shallow models in an ever-growing number of other fields. In this work, we show that deep models significantly improve the recalibration of PSMs compared to the most accurate and widely-used post-processors, such as Percolator and PeptideProphet. Furthermore, we show that deep learning is able to adaptively analyze complex datasets and features for more accurate universal post-processing, leading to both improved Prosit analysis and markedly better recalibration of recently developed database-search functions.

bioinformatics↗