ArticleNature communications2020
Predicting gene expression using morphological cell responses to nanotopography.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 76 citations in OpenAlex.
- Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models.Nature communications · 2026Article
- PathCLAST: pathway-augmented contrastive learning with attention for interpretable spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- CYRI-B loss promotes enlarged mature focal adhesions and restricts microtubule and ERC1 access to the cell leading edge.Journal of cell science · 2025Article
- Systematic data analysis pipeline for quantitative morphological cell phenotyping.Computational and structural biotechnology journal · 2024Review
- Label-free live cell recognition and tracking for biological discoveries and translational applications.Npj imaging · 2024Review
- Cell identity revealed by precise cell cycle state mapping links data modalities.bioRxiv : the preprint server for biology · 2024Article
- MVST: Identifying spatial domains of spatial transcriptomes from multiple views using multi-view graph convolutional networks.PLoS computational biology · 2024Article
- Evaluating batch correction methods for image-based cell profiling.Nature communications · 2024Article
- xSiGra: explainable model for single-cell spatial data elucidation.Briefings in bioinformatics · 2024Article
- Dynamic regulation of stem cell adhesion and differentiation on degradable piezoelectric poly (L-lactic acid) (PLLA) nanofibers.Biomedical engineering letters · 2024Article
- Bioprinting Soft 3D Models of Hematopoiesis using Natural Silk Fibroin-Based Bioink Efficiently Supports Platelet Differentiation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Evaluating batch correction methods for image-based cell profiling.bioRxiv : the preprint server for biology · 2024Article
- NanoIEA: A Nanopatterned Interdigitated Electrode Array-Based Impedance Assay for Real-Time Measurement of Aligned Endothelial Cell Barrier Functions.Advanced healthcare materials · 2024Article
- Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.Nature communications · 2023Article
- Reversing pathological cell states: the road less travelled can extend the therapeutic horizon.Trends in cell biology · 2023Review
- Predicting reprogramming-related gene expression from cell morphology in human induced pluripotent stem cells.Molecular biology of the cell · 2023Article
- A review on deep learning applications in highly multiplexed tissue imaging data analysis.Frontiers in bioinformatics · 2023Review
- Tissue-engineered tendon nano-constructs for repair of chronic rotator cuff tears in large animal models.Bioengineering & translational medicine · 2023Article
- TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.Genomics, proteomics & bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cells respond in complex ways to their environment, making it challenging to predict a direct relationship between the two. A key problem is the lack of informative representations of parameters that translate directly into biological function. Here we present a platform to relate the effects of cell morphology to gene expression induced by nanotopography. This platform utilizes the 'morphome', a multivariate dataset of cell morphology parameters. We create a Bayesian linear regression model that uses the morphome to robustly predict changes in bone, cartilage, muscle and fibrous gene expression induced by nanotopography. Furthermore, through this model we effectively predict nanotopography-induced gene expression from a complex co-culture microenvironment. The information from the morphome uncovers previously unknown effects of nanotopography on altering cell-cell interaction and osteogenic gene expression at the single cell level. The predictive relationship between morphology and gene expression arising from cell-material interaction shows promise for exploration of new topographies.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.