@clintomics profile picture
Clint Miller @clintomics

Human genomicist, Assoc Prof @UVA. Deconvolving complex cardiovascular diseases using systems genetics and single-cell omics.

Joined April 2013
  • Tweets 2,393
  • Following 531
  • Followers 1,566
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Latest news feature in @NatureBiotech on recent developments in spatial biology to unravel disease mechanisms, with insights from experts in field @aruthak @NeBanovich @AI4Pathology @PDulaiMD Jasmine Plummer, Amanda Orr etc nature.com/articles/s41587-0…

Beyond cell atlases: spatial biology reveals mechanisms behind disease

Nature Biotechnology - As the next generation of spatial transcriptomics tools hits the market, researchers are uncovering previously unknown interactions that could transform clinical research.

nature.com
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ATVB: An AHA Journal@atvbahajournals
1 Apr 2025
The 9p21 risk locus — the first and most impactful CAD genetic risk factor in humans — drives VSMCs to adopt an osteochondrogenic state, promoting calcification @clintomics @valelosardo ahajrnls.org/3E59c3h
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Valentina Lo Sardo@valelosardo
30 Mar 2025
Thrilled to share the🥇paper from the lab @atvbahajournals. Here we continue to unravel the mysteries of the 9p21.3 CAD locus showing it drives VSMC transition into an osteochondrogenic state, promoting calcification. @clintomics @UWMadisonCRB ahajournals.org/doi/10.1161/…

The 9p21.3 Coronary Artery Disease Risk Locus Drives Vascular Smooth Muscle Cells to an Osteochon...

BACKGROUND: Genome-wide association studies have identified common genetic variants at ≈300 human genomic loci linked to coronary artery disease susceptibility. Among these genomic regions, the most...

ahajournals.org
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Athero-Express@atheroexpress
7 Mar 2025
🚀 Thrilled to announce our latest #preprint on intraplaque haemorrhage (IPH) quantification and molecular 🧬characterisation of carotid plaques using deep learning. A thread 🧵👇🏽
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Jeff Saucerman@jsauce7
11 Feb 2025
Trump's slashing of NIH indirects blocked for now in 22 states but not VA. Will the "peoples protector" Attorney General @JasonMiyaresVA stand up for Virginia researchers and doctors and their work to cure cancer, heart disease, diabetes, and more?
Kyle Cheney
@kyledcheney
10 Feb 2025
JUST IN: A federal judge in Massachusetts has blocked the Trump administration's rate change to NIH grants.
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Huge congrats to PhD student @John_S_IV for successfully defending his thesis today! Thanks to the committee and everyone for their support. @shefflab @uva_bims @MedicineUVA
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New model for multimodal spatial omics analysis, MISO, performs feature extraction, clustering and handles large-scale omics data. Includes extensive benchmarking and application to various cancer types. ⁦@DrMingyaoLi⁩ ⁦@naturemethods⁩ nature.com/articles/s41592-0…

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New expression foundation model, GET, predicts gene expression in unseen cell types and context specific TF interaction networks - pretrained on chromatin accessibility data from 213 human adult and fetal cell types. @nature @ericxing @RabadanColumbia nature.com/articles/s41586-0…

A foundation model of transcription across human cell types

Nature - A foundation model learns transcriptional regulatory syntax from chromatin accessibility and sequence data across a range of cell types to predict gene expression and transcription factor...

nature.com
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New generative model, IMPA, predicts cell morphology responses to genetic and drug perturbations, enhancing phenotypic screening and drug discovery. Corrects batch effects and models unseen perturbations. @NatureComms @mo_lotfollahi @fabian_theis doi.org/10.1038/s41467-024-5…

Predicting cell morphological responses to perturbations using generative modeling

Nature Communications - Predicting morphological impacts of perturbations using computational methods can aid treatment discovery. Here, authors present IMPA, a generative model that predicts...

nature.com
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New single-cell analysis framework, LEMUR, disentangles covariates and latent cell states from multi-condition data to predict counterfactual gene expression and identify cell neighborhoods with similar DEGs without clustering. doi.org/10.1038/s41588-024-0… @NatureGenet @wolfgangkhuber

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R package: bioconductor.org/packages/le… Code: github.com/const-ae/lemur-Pa… Python implementation: github.com/const-ae/pylemur
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Delineating the effective use of self-supervised learning in single-cell genomics ⁦@NatMachIntell⁩ ⁦@fabian_theis⁩ nature.com/articles/s42256-0…

Delineating the effective use of self-supervised learning in single-cell genomics

Nature Machine Intelligence - Self-supervised learning techniques are powerful assets for enabling deep insights into complex, unlabelled single-cell genomic data. Richter et al. here benchmark the...

nature.com
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Very grateful and humbled to receive promotion to tenure @MedicineUVA! Huge thanks to my supportive mentors, colleagues, family, and talented trainees who continue to inspire me along this journey. Here’s to the work ahead!
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Vladimir Shitov@shitov_happens
13 Dec 2024
The number of cells in single-cell transcriptomics studies grew exponentially over the years. But what about the number of donors? Let me tell you the story of my contribution to the fantastic paper of @SikkemaLisa and @KHrovatin. Read the paper: nature.com/articles/s41592-0… 1/12

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Nice benchmarking study of 10 different foundation models for single cell data analysis..important best practices, limitations and insights to guide future applications @HongyuZhao2 biorxiv.org/content/10.1101/…
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As expected the performance of scFMs is highly task specific..they perform well in certain tasks after fine tuning, eg cell annotation, but worse in gene regulatory networks.
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For simulation tasks scDesign3 outperformed scFMs in simulating reference based scRNA-seq datasets @SongDongyuan @jsb_ucla
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Stephen Quake@StephenQuake
9 Dec 2024
Virtual cell models have the potential to transform biological research. Today @ChanZuckerberg released an initial set of models, including scGenePT and SubCell, designed to be easy to run and build upon. This is a really exciting time in biology. czi.co/4g1jTSa

Early Access to CZI's AI-Powered Cell Models

We’re making it easier for biologists and machine learning researchers to collaborate with cell models that are easy-to-use build upon to accelerate the next big breakthrough.

chanzuckerberg.com
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New method CellANOVA recovers lost biological signals and evaluates global and gene level distortions introduced from common single-cell batch correction algorithms ⁦@NatureBiotech⁩ github.com/Janezjz/cellanova nature.com/articles/s41587-0…

GitHub - Janezjz/cellanova: CellANOVA: Cell State Space Analysis of Variance for signal recovery in...

CellANOVA: Cell State Space Analysis of Variance for signal recovery in single cell batch integration - Janezjz/cellanova

github.com
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Junedh M. Amrute@junedh_amrute
14 Nov 2024
We use Multiome and HiC to build a comprehensive single cell variant to enhancer to gene map for coronary artery disease. Join effort with @PaulChungrohLee and IttaiEres in Nate Stitziel Lab and in collaboration with @Amgen @WUSTLmed medrxiv.org/content/10.1101/…

Single cell variant to enhancer to gene map for coronary artery disease

Although genome wide association studies (GWAS) in large populations have identified hundreds of variants associated with common diseases such as coronary artery disease (CAD), most disease-associa...

medrxiv.org
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