Mechanomics
This collection contains materials supporting findings presented in the following manuscript:
Urbanska, M., Ge, Y. et al., 2021 bioRxiv
De novo identification of universal cell mechanics regulators
https://doi.org/10.1101/2021.04.26.441418
Abstract
Mechanical proprieties determine many cellular functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors governing cell mechanical phenotype is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between mechanical phenotype changes and the genes involved in driving them. We employ a machine learning-based discriminative network analysis method termed PC-corr to associate cell mechanical states, measured by real-time deformability cytometry (RT-DC), with large‑scale transcriptome datasets ranging from stem cell development to cancer progression, and originating from different murine and human tissues. By intersecting the discriminative networks inferred from two selected datasets, we identify a conserved module of five genes with putative roles in the regulation of cell mechanics. We validate the power of the individual genes to discriminate between soft and stiff cell states in silico, and demonstrate experimentally that the top scoring gene, CAV1, changes the mechanical phenotype of cells when silenced or overexpressed. The data-driven approach presented here has the power of de novo identification of genes involved in cell mechanics regulation and paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.
The included datasets correspond to the figures in manuscript in a following manner:
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Mechanical characterisation using real-time deformability cytometry (RT-DC)
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• Mechanomics RT-DC Set 02 - Carcinoma
https://doi.org/10.6084/m9.figshare.14473344
Figure 1b, Supplementary Figure 2b
• Mechanomics RT-DC Set 03 - HSPCs
https://doi.org/10.6084/m9.figshare.14473389
Figure 1c, Supplementary Figure 2c
• Mechanomics RT-DC Set 04 - MCF10A-PIK3CA
https://doi.org/10.6084/m9.figshare.14473512
Figure 1d, Supplementary Figure 2d
• Mechanomics RT-DC Set 05 - iPSCs
https://doi.org/10.6084/m9.figshare.14473581
Figure 1e, Supplementary Figure 2e
• Mechanomics RT-DC Set 06 - Developing Neurons
https://doi.org/10.6084/m9.figshare.14473554
Figure 1f, Supplementary Figure 2f
• Mechanomics RT-DC Supplementary - MEFs CAV1KO
https://doi.org/10.6084/m9.figshare.14481405
Supplementary Figure 7
• Mechanomics RT-DC Validation - TGBC CAV1 KD esiRNA
https://doi.org/10.6084/m9.figshare.14481411
Figure 4c, Supplementary Figure 9
• Mechanomics RT-DC Validation - TGBC CAV1 KD ONTARGET
https://doi.org/10.6084/m9.figshare.14481417
Figure 4c, Supplementary Figure 9
• Mechanomics RT-DC Validation - ECC4 CAV1 OE
https://doi.org/10.6084/m9.figshare.14483082
Figure 4e–f, Supplementary Figure 9
• Mechanomics RT-DC Validation - TGBC CAV1 OE
https://doi.org/10.6084/m9.figshare.14481432
Figure 4e–f, Supplementary Figure 9
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Mechanical characterisation using Atomic Force Microscopy (AFM)
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• Mechanomics AFM Set 02 - ECC4 vs TGBC
https://doi.org/10.6084/m9.figshare.14483235
Supplementary Figure 8
• Mechanomics AFM Validation - TGBC CAV1 KD esiRNA
https://doi.org/10.6084/m9.figshare.14483202
Supplementary Figure 10
• Mechanomics AFM Validation - MCF10A-ER-Src
https://doi.org/10.6084/m9.figshare.14483259
Supplementary Figure 11
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Analysis Code
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• Mechanomics Code - JVT
contains codes and data necessary for the estimation of Joint-View trustworthiness (JVT) values presented in the manuscript Table 4.
https://doi.org/10.6084/m9.figshare.20123159
