Biology subjects

Byers, J. M.

Publications and source records attributed to Byers, J. M..

4 recordsLinked to original sources

bioRxiv2024

A Self-Supervised Learning Approach for High Throughput and High Content Cell Segmentation

In principle, AI-based algorithms should enable rapid and accurate cell segmentation in high-throughput settings. However, reliance on large datasets, human input, and computational expertise, along with issues of limited generalizability and the necessity for specialized training are notable drawbacks of nominally "automated" segmentation tools. To overcome this roadblock, we introduce an innovative, user-friendly self-supervised learning method (SSL) for pixel classification that requires no dataset-specific modifications or curated labelled data sets, thus providing a more streamlined cell segmentation approach for high-throughput and high-content research. We demonstrate that our algorithm meets the criteria of being fully automated with versatility across various magnifications, optical modalities and cell types. Moreover, our SSL algorithm is capable of identifying complex cellular structures and organelles which are otherwise easily missed, thereby broadening the machine learning applications to high-content imaging. Our SSL technique displayed consistent F1 scores across segmented images, with scores ranging from 0.831 to 0.876, outperforming the popular Cellpose algorithm, which showed greater variance in F1 scores from 0.645 to 0.8815, mainly due to errors in segmentation. On average, our SSL method achieved an F1 score of 0.852 {+/-}0.017, exceeding Cellposes average of 0.804 {+/-}0.08. This novel SSL method not only advances segmentation accuracy but also minimizes the need for extensive computational expertise and data security concerns, making it easier for biological researchers to incorporate automated segmentation into their studies.

bioengineering↗

bioRxiv2021

Interfacing Live Cells with Surfaces: A Concurrent Control Technique for Quantifying Surface Ligand Activity

Surface ligand activity is a key design parameter for successfully interfacing surfaces with cells - whether in the context of in vitro investigations for understanding cellular signaling pathways or more applied applications in drug delivery and medical implants. Unlike other crucial surface parameters, such as stiffness and roughness, surface ligand activity currently lacks a standardized measurement approach that can be readily paired with live cell investigations. To fill this void, we have developed a concurrent control technique for characterizing in vitro ligand surface activity. Pairs of gold-coated glass chips were biofunctionalized with RGD ligand in a parallel workflow: one chip for in vitro applications and the other for surface plasmon resonance (SPR) based RGD activity characterization. Recombinant V{beta}3 integrins were injected over the SPR chip surface as mimics of the cellular membrane bound receptors and the resulting binding kinetics parameterized to quantify ligand activity. These activity measurements were correlated with cell morphological features, measured by interfacing MDA-MB-231 cells with the in vitro chip surfaces on the live cell microscope. We show that the SPR concurrent control approach has multiple advantages based on the facts that SPR is a standardized technique and has the sensitivity to measure ligand activity across the most relevant range of extracellular surface densities. Furthermore, by pairing both SPR and in vitro approaches, a comparison of the results can provide biological insights into the nature of cellular adhesion and dynamics.

cell biology↗

bioRxiv2021

A Self-Supervised Machine Learning Approach for Objective Live Cell Segmentation and Analysis

Machine learning algorithms hold the promise of greatly improving live cell image analysis by way of (1) analyzing far more imagery than can be achieved by more traditional manual approaches and (2) by eliminating the subjective nature of researchers and diagnosticians selecting the cells or cell features to be included in the analyzed data set. Currently, however, even the most sophisticated model based or machine learning algorithms require user supervision, meaning the subjectivity problem is not removed but rather incorporated into the algorithms initial training steps and then repeatedly applied to the imagery. To address this roadblock, we have developed a self-supervised machine learning algorithm that recursively trains itself directly from the live cell imagery data, thus providing objective segmentation and quantification. The approach incorporates an optical flow algorithm component to self-label cell and background pixels for training, followed by the extraction of additional feature vectors for the automated generation of a cell/background classification model. Because it is self-trained, the software has no user-adjustable parameters and does not require curated training imagery. The algorithm was applied to automatically segment cells from their background for a variety of cell types and five commonly used imaging modalities - fluorescence, phase contrast, differential interference contrast (DIC), transmitted light and interference reflection microscopy (IRM). The approach is broadly applicable in that it enables completely automated cell segmentation for long-term live cell phenotyping applications, regardless of the input imagerys optical modality, magnification or cell type.

bioinformatics↗

bioRxiv2020

Robust Optical Flow Algorithm for General, Label-free Cell Segmentation

Cell segmentation is crucial to the field of cell biology, as the accurate extraction of cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. Here, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments without the need of labels. By leveraging cell movement in time-lapse imagery as a means to distinguish cells from their background and augmenting the output with machine vision operations, our algorithm reduces the number of adjustable parameters needed for optimization to two. The code is packaged within a MATLAB executable file, offering an accessible means for general cell segmentation typically unavailable in most cell biology laboratories.

cell biology↗