Monolithic quartz platform for cellular contact guidance

Robitaille, Michael; Christodoulides, Joseph A.; Liu, Jinny L.; Kang, Wonmo; Byers, Jeff M.; Doctor, Katarina Z.; Kozak, Dmitry A.; Raphael, Marc P.

doi:10.1557/mrc.2020.15

Cited by 6 publications

(17 citation statements)

References 32 publications

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“…The fabrication process is given in detail elsewhere (Robitaille, Christodoulides, Liu, Kang, Byers, Doctor, et al, 2020). Briefly, 20 nm of chrome was e‐beam evaporated onto fused silica #1.5 coverslips (SPI, GE 124) and coated with AZ1518 photoresist (Microchemicals GmbH).…”

Section: Methodsmentioning

confidence: 99%

“…While there are several studies that support this focal adhesion confinement model (den Braber et al, 1998; Saito et al, 2014), it is not clear how the confinement model, which is centered around lateral ( x , y ) topographical constraints, is consistent with the overall larger trend observed of a depth‐dependent ( z ) cellular response in the literature (Leclech & Barakat, 2021). To investigate the underlying mechanisms of the depth‐related contact guidance response, we have expanded upon a previously established fabrication method that is suited for deep etching to include a set of monolithic quartz chips with depths of 330, 725, and 1000 nm (Robitaille, Christodoulides, Liu, Kang, Byers, Doctor, et al, 2020). This fabrication method is ideal as it allows for tightly controlled surface roughness (sub nm) to avoid competing topographical cues (i.e., roughness changing within a given topographical condition, or with altered topographical conditions).…”

Section: Introductionmentioning

confidence: 99%

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Topographical depth reveals contact guidance mechanism distinct from focal adhesion confinement

Robitaille,

Kim,

Christodoulides

et al. 2024

Cytoskeleton

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Cellular response to the topography of their environment, known as contact guidance, is a crucial aspect to many biological processes yet remains poorly understood. A prevailing model to describe cellular contact guidance involves the lateral confinement of focal adhesions (FA) by topography as an underlying mechanism governing how cells can respond to topographical cues. However, it is not clear how this model is consistent with the well‐documented depth‐dependent contact guidance responses in the literature. To investigate this model, we fabricated a set of contact guidance chips with lateral dimensions capable of confining focal adhesions and relaxing that confinement at various depths. We find at the shallowest depth of 330 nm, the model of focal adhesion confinement is consistent with our observations. However, the cellular response at depths of 725 and 1000 nm is inadequately explained by this model. Instead, we observe a distinct reorganization of F‐actin at greater depths in which topographically induced cell membrane deformation alters the structure of the cytoskeleton. These results are consistent with an alternative curvature‐hypothesis to explain cellular response to topographical cues. Together, these results indicate a confluence of two molecular mechanisms operating at increased induced membrane curvature that govern how cells sense and respond to topography.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Topographical depth reveals contact guidance mechanism distinct from focal adhesion confinement

Robitaille,

Kim,

Christodoulides

et al. 2024

Cytoskeleton

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“…All mammalian cells were cultured in DMEM (ATCC, #30-2002) supplemented with 10% fetal bovine serum (ATCC, #30-2020) at 37 °C and 5% CO 2 , and all imaging of mammalian cells was conducted under serum free conditions (DMEM alone). Hs27 fibroblasts (ATCC, #CRL 1634) were imaged on planar sections of quartz contact guidance chips as previously described 28 . MDA-MB-231 cells (ATCC #HTB-26) were imaged on glass bottomed well plates coated with 25 µg/mL Fibronectin (Gibco #33016015) or functionalized gold coated coverslips as previously described 29 .…”

Section: Methodsmentioning

confidence: 99%

Self-supervised machine learning for live cell imagery segmentation

et al. 2022

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Segmenting single cells is a necessary process for extracting quantitative data from biological microscopy imagery. The past decade has seen the advent of machine learning (ML) methods to aid in this process, the overwhelming majority of which fall under supervised learning (SL) which requires vast libraries of pre-processed, human-annotated labels to train the ML algorithms. Such SL pre-processing is labor intensive, can introduce bias, varies between end-users, and has yet to be shown capable of robust models to be effectively utilized throughout the greater cell biology community. Here, to address this pre-processing problem, we offer a self-supervised learning (SSL) approach that utilizes cellular motion between consecutive images to self-train a ML classifier, enabling cell and background segmentation without the need for adjustable parameters or curated imagery. By leveraging motion, we achieve accurate segmentation that trains itself directly on end-user data, is independent of optical modality, outperforms contemporary SL methods, and does so in a completely automated fashion—thus eliminating end-user variability and bias. To the best of our knowledge, this SSL algorithm represents a first of its kind effort and has appealing features that make it an ideal segmentation tool candidate for the broader cell biology research community.

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“…Our group recently introduced a contact guidance platform capable of integrating with nearly all forms of live-cell microscopy [37], which generates a unique problem for accurate cell segmentation against the backdrop of etched grooves that serve as topographical cues.…”

Section: Plos Onementioning

confidence: 99%

“…Focus was stabilized for the multi-hour long experiments using an integrated hardware-based focus correction device (Zeiss Definite Focus). All mammalian cell line experiments were done in serum free media, and conducted on either glass-bottomed well plates/petri dishes or in the case of contact guidance structures, quartz chips detailed previously [37]. The Dictyostelium imaging was conducted on glass bottomed petri dishes in HL5 culture media.…”

Section: Microscope/in Vitro Set Upsmentioning

confidence: 99%

Robust optical flow algorithm for general single cell segmentation

et al. 2022

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Cell segmentation is crucial to the field of cell biology, as the accurate extraction of single-cell morphology, migration, and ultimately behavior from time-lapse live cell imagery are of paramount importance to elucidate and understand basic cellular processes. In an effort to increase available segmentation tools that can perform across research groups and platforms, we introduce a novel segmentation approach centered around optical flow and show that it achieves robust segmentation of single cells by validating it on multiple cell types, phenotypes, optical modalities, and in-vitro environments with or without 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 manual optimization to two. We show that this approach offers the advantage of quicker processing times compared to contemporary machine learning based methods that require manual labeling for training, and in most cases achieves higher quality segmentation as well. This algorithm is packaged within MATLAB, offering an accessible means for general cell segmentation in a time-efficient manner.

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Monolithic quartz platform for cellular contact guidance

Abstract: Abstract

Cited by 6 publications

References 32 publications

Topographical depth reveals contact guidance mechanism distinct from focal adhesion confinement

Topographical depth reveals contact guidance mechanism distinct from focal adhesion confinement

Self-supervised machine learning for live cell imagery segmentation

Robust optical flow algorithm for general single cell segmentation

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