Manolis Kellis

Dissecting disease mechanism. Single-cell, Epigenomics, Regulatory Genomics, Disease Genetics, Brain, Cancer, Metabolism. @MIT Prof, @MIT_CSAIL, @BroadInstitute

[Manolis Kellis @manoliskellis]

Kellis Lab is hiring! New grants in #Alzheimers #Schizophrenia #Cancer #EHR #SingleCell #Genome #Epigenome #Transcription #Disease integration. Computational & experimental positions, profiling and circuitry dissection. Join us! @MIT_CSAIL @BroadInstitute https://t.co/rHrKNJ7lJH

[The Science of Parkinson's @ScienceofPD]

Single-cell profiling of 109 human striatum samples by @LinvilleRaleigh @__ben_james__ @manoliskellis & colleagues maps human striatal neuron diversity & subregion specialization; scMOSAIC unravels subregional & cell-type vulnerabilities in #Huntingtons
https://t.co/xlU3v8K9q5

[Riley Mangan @riley_mangan]

I'm thrilled to share that a study I co-led with @EleleRaven, leveraging T2T human and ape genomes to expand and refine our identification of rapidly evolved regions in the human genome, is now published in Cell Genomics!

[Science in Boston @ScienceinBoston]

A region of the brain called the #striatum is critical for many cognitive and motor functions.

Drs. Heiman, @manoliskellis ,& Gabuzda at @MIT made a new atlas of neurons found within the striatum using single-cell RNA sequencing among other techniques: https://t.co/NvPmHRG13a

[Manolis Kellis @manoliskellis]

Excited to share our Cross-Species Single-cell Atlas of the Human Brain's Striatum, the brain region that integrates action planning, reward, motivation, decision-making, movement, learning, and reinforcement, implicated in schizophrenia, depression, addiction, and Huntington's Disease: https://t.co/jpbT7LYdYg

Our map spans 109 samples, 69 donors, and 670,000 nuclei. We identify 31 neuronal subpopulations, including 9 types of medium spiny neurons, and rare D1 interface MSNs forming island-like structures. Patch-clamp recordings show outlier MSNs have smaller somata and stronger hyperpolarization-activated currents than their neighbors.

We show that the dorsolateral-ventromedial gradient shapes: receptor repertoires, disease gene expression, vulnerability.

Across 124 GWAS, we show genetic risk for neuropsychiatric, cognitive and addiction traits concentrates in MSNs and is depleted in glia and vascular cells. Schizophrenia, bipolar and ADHD risk genes are dorsally enriched, and so, unexpectedly, are opioid use disorder risk genes — even though opioid signaling itself is ventrally biased, suggesting heritable risk and acute drug action may engage different parts of the striatum.

Only a quarter of the human gradient genes are similarly zonated in mice. Several drug targets sit in different cells across species, including the mu-opioid receptor, the M5 muscarinic receptor, and the GLP-1 receptor, with important implications for preclinical screening. Profiling chronic antipsychotic exposure in mice, we find the transcriptional response strongly biased toward ventral MSNs.

Finally, we connect genotype to phenotype at single-cell resolution. Our new method, scMOSAIC, reads HTT CAG repeat length and gene expression from the same nucleus — and finds that repeat length varies along that same dorsolateral-ventromedial axis.

Huge congrats to Raleigh Linville @LinvilleRaleigh , Benjamin James @__ben_james__ , who carried this from start to finish, and to co-senior authors #MyriamHeiman and #DanaGabuzda, and Kiki Galani, Li-Lun Ho, Stu Fass, James Cameron, Brent Fitzwalter, Erica Engelberg-Cook, Sebastian Pineda, Dennis Dickson, Deborah Mash, Gustavo Turecki, Vanessa Wheeler, and Veronica Alvarez, across Massachusetts Institute of Technology, The Picower Institute for Learning and Memory, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), Broad Institute of MIT and Harvard, Dana-Farber Cancer Institute, The National Institutes of Health, Mass General Brigham, Mayo Clinic, McLean Hospital, McGill University, and beyond.

Huge thanks to the #SCORCH consortium and the brain banks in the US and Canada, and a huge thanks to the donors and their families. Every cell in this atlas came from someone who decided their brain should go on being useful. The data are public, and we hope other labs take them further than we can.

[Riley Mangan @riley_mangan]

I'm very excited to share the preprint of the central project of my postdoc, titled "Sequence-to-function deep learning decodes human cis-regulatory evolution" available today on BioRxiv. Link below

[RECOMB Conference Series @RECOMBconf]

📢 We are thrilled to announce the keynote speakers for #RECOMB2026:

🧬 Sara Mostafavi
🧬 Manolis Kellis
🧬 Paul Medvedev
🧬 Alexandros Stamatakis

Join us in Thessaloniki for inspiring talks from leaders in computational biology.

[Hillary Lin, MD @HillaryLinMD]

The future of longevity medicine will not be decided by the loudest protocol.

It will be decided by standards.

Which biomarkers are real enough to act on? Which interventions change outcomes, not just pathways? Which patients benefit, which patients are exposed to cost and risk, and how do we tell the difference before the market decides for us?

That is the lens I’m bringing to the 2026 Aging Code Summit in Cambridge, May 26-27, during Boston Tech Week.

I’ll be speaking on Day 2 about evidence-based best practices in longevity medicine, from the perspective of an internal medicine physician trying to turn a very noisy field into something clinically useful.

The science is finally getting concrete. Aging biology is becoming therapeutics, diagnostics, AI models, biomarker systems, skin and immune longevity, neurodegeneration work, and new company formation. The next question is harder: what deserves to become medicine?

Hosted by @LongevityGlobal in partnership with @Mindvyne and @3cubedAi, with speakers including @agingdoc1, @manoliskellis, Li-Huei Tsai at @MIT_Picower, @kpfortney at @bioagelabs, Sharon Rosenzweig-Lipson at @lifebiosciences, @mahdi_moqri at @agingbiomarkers, Amy Proal @microbeminded2 at @polybioRF, Saranya Wyles @drwyles_derm, Jens Eckstein @AkikoaCom at @hevolution_f, @DrGlorioso at @NeuroAgeTX, @JamieHeywood, @AldenScientific, Fiona Miller @quadrascope, @Dr_RayMak, José Navarro Betancourt, Justin Taylor, Noriko Yokoi, Daniel Dacey, Spring Behrouz, Raghav Sehgal @rv_sehgal, Jay Luthar, @usnehal, @tomzuber, Robin Mansukhani, Fernanda Cerqueira, David Hall, Yeh-Chuin Poh, Salah Mahmoudi, and @RutaLaukien.

If you are building, funding, prescribing, regulating, or seriously studying longevity medicine, this is the conversation worth having in person.

Event details and registration:
https://t.co/Jr5WK6a5YP
More here:
https://t.co/FaRmW0dmBv
https://t.co/wJVgcDGDDz

[Arthur Liang @arthliang]

I’m really excited to be @iclr_conf this week presenting our work on substructure-aware protein modeling! DM me if you want to chat about protein models, baking biological priors into ML architectures, and anything bioML for experimentalists!

We built Magneton, a model-agnostic framework that distills decades of curated substructure knowledge into any pretrained encoder. We find that substructure signal is complementary to both sequence and structure as well as helps models generalize to unseen substructure types. Come say hi at our poster on Saturday from 10:30AM-1:00PM @ Pavilion 3 P3-#1006!

This work is near and dear to my heart because it’s my first (co)first-author main conference paper from my undergrad. A heartfelt shoutout to my mentors Robert Calef, @manoliskellis, @marinkazitnik for all their support!

Website: https://t.co/bvC9WM5lWZ
Paper: https://t.co/3hYEbmEc0V

[Front Line Genomics @FLGenomics]

We’re delighted to share that @ManolisKellis (Professor of Computer Science, MIT) will be delivering a keynote talk on #AI for genomic medicine at The Festival of Genomics, Biodata & AI in Boston this summer. More info: https://t.co/bIYMDfJktO #FOGBoston #genomics

[Yosuke Tanigawa @yk_tani]

I’m happy to share our latest preprint, sc4D, led by the incredible Ishir Rao @ishirraov!

We developed a new #computational approach to model #spatiotemporal #transcriptional #dynamics in disease progression 🧬.

[Sylvan Baca MD PhD (Hiring!) @SylvanBacaLab]

Excited to share our latest preprint by Alexis Yang and Gary Lee, in collab with the @jberchuck lab: https://t.co/cb3pRjketO. We found that transcription of enhancers (a mark of activation) causes distinct fragmentation patterns in #cfDNA.

[Manolis Kellis @manoliskellis]

@ZeTristanShi Wonderful meeting you ZeChuan! :-)

[Manolis Kellis @manoliskellis]

So proud of #RileyMangan and #NikithaThoduguli presenting platform talks at #ASHG25 today on #SingleCell phenotypes for #Alzheimers, and #DeepLearning #Evolution of human cis-regulatory elements. Spread the word! @MIT @BroadInstitute @HarvardMed

[Manolis Kellis @manoliskellis]

MIT Course announcement: Machine Learning for Computational Biology #MLCB25
Fall'24 Lecture Videos: https://t.co/tA3zeuIF7g
Fall'24 Lecture Notes: https://t.co/C3WmXZuQur
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery. 
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis @manoliskellis, Prof. Eric Alm @ejalm, TAs: Ananth Shyamal, Shitong Luo @luost26
Course website: https://t.co/ateGr6xKLM
@MIT @MITEECS @MITdeptofBE @MITCSBPhD @MIT_CSAIL @Harvard @HarvardMed @BroadInstitute

[Waggoner Lab @LabWaggoner]

Mature and migratory dendritic cells promote immune infiltration and response to anti-PD-1 checkpoint blockade in metastatic melanoma @NatureComms @gmboland @manoliskellis
https://t.co/ZaxZGRtcVm

[Manolis Kellis @manoliskellis]

MIT Course announcement: Machine Learning for Computational Biology #MLCB25
Fall'24 Lecture Videos: https://t.co/tA3zeuIF7g
Fall'24 Lecture Notes: https://t.co/C3WmXZuQur
(a) Genomes: Statistical genomics, gene regulation, genome language models, chromatin structure, 3D genome topology, epigenomics, regulatory networks.
(b) Proteins: Protein language models, structure and folding, protein design, cryo-EM, AlphaFold2, transformers, multimodal joint representation learning.
(c) Therapeutics: Chemical landscapes, small-molecule representation, docking, structure-function embeddings, agentic drug discovery, disease circuitry, and target identification.
(d) Patients: Electronic health records, medical genomics, genetic variation, comparative genomics, evolutionary evidence, patient latent representation, AI-driven systems biology.
Foundations and frontiers of computational biology, combining theory with practice. Generative AI, foundation models, machine learning, algorithm design, influential problems and techniques, analysis of large-scale biological datasets, applications to human disease and drug discovery. 
First Lecture: Thu Sept 4 at 1pm in 32-144
With: Prof. Manolis Kellis @manoliskellis, Prof. Eric Alm @ejalm, TAs: Ananth Shyamal, Shitong Luo @luost26
Course website: https://t.co/ateGr6xKLM
@MIT @MITEECS @MITdeptofBE @MITCSBPhD @MIT_CSAIL @Harvard @HarvardMed @BroadInstitute

[Manolis Kellis @manoliskellis]

Today was my last lecture for @MIT #ComputationalBiology: #Genomes, #Networks, #Evolution, #Health. I recorded each and immediately posted online here: https://t.co/z7eouWlosl
Please do share, and let me know which topics need more explanations, clarifications, and corrections!

[Sofoklis Kyriazakos 👊 @skyriazakos]

I could never imagine combining deep conversations on #AI & #longevity with the beauty of my beloved Andros… until the AI Longevity Salon, hosted by @manoliskellis - where science met philosophy & ideas on longevity flowed.
Thanks, Manolis, for the photos & the inspiring event.