Simultaneous CRISPR screening and spatial transcriptomics reveal intracellular, intercellular, and functional transcriptional circuits

Loϊc Binan,1,2 Aiping Jiang,3,4,5 Serwah A. Danquah,1,6,12 Vera Valakh,1,6,13 Brooke Simonton,2,14 Jon Bezney,2,15 Robert T. Manguso,3,4,5 Kathleen B. Yates,3,4,5 Ralda Nehme,6 Brian Cleary,7,8,9,10,11,* and Samouil L. Farhi1,16,*

1 Spatial Technology Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

2 Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

3 Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA

4 Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA 02144, USA

5 Department of Medicine, Harvard Medical School, Boston, MA 02115, USA

6 Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA

7 Faculty of Computing and Data Sciences, Boston University, Boston, MA 02215, USA

8 Department of Biology, Boston University, Boston, MA 02215, USA

9 Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA

10 Program in Bioinformatics, Boston University, Boston, MA 02215, USA

11 Biological Design Center, Boston University, Boston, MA 02215, USA

12 Present address: Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA

13 Present address: Stoke Therapeutics, Bedford, MA, USA

14 Present address: The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

15 Present address: Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA

16 Lead contact

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (B.C.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (S.L.F.)

https://doi.org/10.1016/j.cell.2025.02.012

SUMMARY

Pooled optical screens have enabled the study of cellular interactions, morphology, or dynamics at massive scale, but they have not yet leveraged the power of highly plexed single-cell resolved transcriptomic readouts to inform molecular pathways. Here, we present a combination of imaging spatial transcriptomics with parallel optical detection of in situ amplified guide RNAs (Perturb-FISH). Perturb-FISH recovers intracellular effects that are consistent with single-cell RNA-sequencing-based readouts of perturbation effects (Perturb-seq) in a screen of lipopolysaccharide response in cultured monocytes, and it uncovers intercellular and density-dependent regulation of the innate immune response. Similarly, in three-dimensional xenograft models, Perturb-FISH identifies tumor-immune interactions altered by genetic knockout. When paired with a functional readout in a separate screen of autism spectrum disorder risk genes in human-induced pluripotent stem cell (hIPSC) astrocytes, Perturb-FISH shows common calcium activity phenotypes and their associated genetic interactions and dysregulated molecular pathways. Perturb-FISH is thus a general method for studying the genetic and molecular associations of spatial and functional biology at single-cell resolution.

Resurrection of endogenous retroviruses during aging reinforces senescence

Xiaoqian Liu,1,5,6,7,21 Zunpeng Liu,1,5,7,21 Zeming Wu,2,5,7,21 Jie Ren,3,5,7,21 Yanling Fan,3,7 Liang Sun,9 Gang Cao,8 Yuyu Niu,11,12,13 Baohu Zhang,1,7 Qianzhao Ji,2,7 Xiaoyu Jiang,2,7 Cui Wang,3,7 Qiaoran Wang,3,7 Zhejun Ji,1,5,7 Lanzhu Li,2,7 Concepcion Rodriguez Esteban,18 Kaowen Yan,2,5,7 Wei Li,4 Yusheng Cai,2,5,7 Si Wang,4,5,7,15,16 Aihua Zheng,7,19 Yong E. Zhang,7,14 Shengjun Tan,14 Yingao Cai,7,14 Moshi Song,2,5,6,7 Falong Lu,7,10 Fuchou Tang,17 Weizhi Ji,11,12,20 Qi Zhou,1,5,6,7,20 Juan Carlos Izpisua Belmonte,18,20 Weiqi Zhang,3,5,7,* Jing Qu,1,5,6,7,* and Guang-Hui Liu2,4,5,6,7,22,*

1State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

 2 State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

3 CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China

4 Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing 100053, China

5 Institute for Stem cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China

6 Beijing Institute for Stem Cell and Regenerative Medicine, Beijing 100101, China

7 University of Chinese Academy of Sciences, Beijing 100049, China

8 State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China

9 NHC Beijing Institute of Geriatrics, NHC Key Laboratory of Geriatrics, Institute of Geriatric Medicine of Chinese Academy of Medical Sciences, National Center of Gerontology/Beijing Hospital, Beijing 100730, China

10 State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing 100101, China

11 State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

12 Yunnan Key Laboratory of Primate Biomedical Research, Kunming, Yunnan 650500, China

13 Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming, Yunnan 650500, China

14 Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

15 Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing Municipal Geriatric Medical Research Center, Beijing 100053, China

16 The Fifth People’s Hospital of Chongqing, Chongqing 400062, China

17 Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China

18 Altos Labs, Inc., San Diego, CA 94022, USA

19 State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

20 Senior author

21 These authors contributed equally

22 Lead contact

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (W.Z.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (J.Q.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (G.-H.L.)

https://doi.org/10.1016/j.cell.2022.12.017

SUMMARY

Whether and how certain transposable elements with viral origins, such as endogenous retroviruses (ERVs) dormant in our genomes, can become awakened and contribute to the aging process is largely unknown. In human senescent cells, we found that HERVK (HML-2), the most recently integrated human ERVs, are unlocked to transcribe viral genes and produce retrovirus-like particles (RVLPs). These HERVK RVLPs constitute a transmissible message to elicit senescence phenotypes in young cells, which can be blocked by neutralizing antibodies. The activation of ERVs was also observed in organs of aged primates and mice as well as in human tissues and serum from the elderly. Their repression alleviates cellular senescence and tissue degeneration and, to some extent, organismal aging. These findings indicate that the resurrection of ERVs is a hallmark and driving force of cellular senescence and tissue aging.

Polarity-guided uneven mitotic divisions control brassinosteroid activity in proliferating plant root cells

Motor and vestibular signals in the visual cortex permit the separation of self versus externally generated visual motion

Mateo Ve´ lez-Fort,1,3 Lee Cossell,1,3 Laura Porta,1 Claudia Clopath,1,2 and Troy W. Margrie1,4,*

1 Sainsbury Wellcome Centre, University College London, London, UK

2 Bioengineering Department, Imperial College London, London, UK

3 These authors contributed equally

4 Lead contact

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

https://doi.org/10.1016/j.cell.2025.01.032

SUMMARY

Knowing whether we are moving or something in the world is moving around us is possibly the most critical sensory discrimination we need to perform. How the brain and, in particular, the visual system solves this motion-source separation problem is not known. Here, we find that motor, vestibular, and visual motion signals are used by the mouse primary visual cortex (VISp) to differentially represent the same visual flow information according to whether the head is stationary or experiencing passive versus active translation. During locomotion, we find that running suppresses running-congruent translation input and that translation signals dominate VISp activity when running and translation speed become incongruent. This cross-modal interaction between the motor and vestibular systems was found throughout the cortex, indicating that running and translation signals provide a brain-wide egocentric reference frame for computing the internally generated and actual speed of self when moving through and sensing the external world.

Mapping the landscape of social behavior

Ugne Klibaite,1,4,* Tianqing Li,2,4 Diego Aldarondo,1,3 Jumana F. Akoad,1 Bence P. O¨ lveczky,1,* and Timothy W. Dunn2,5,*

1  Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA

2  Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA

3  Present address: Fauna Robotics, New York, NY 10003, USA

4  These authors contributed equally

5  Lead contact

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (U.K.), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (B.P.O¨ .), 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (T.W.D.)

https://doi.org/10.1016/j.cell.2025.01.044

SUMMARY

Social interaction is integral to animal behavior. However, lacking tools to describe it in quantitative and rigorous ways has limited our understanding of its structure, underlying principles, and the neuropsychiatric disorders, like autism, that perturb it. Here, we present a technique for high-resolution 3D tracking of postural dynamics and social touch in freely interacting animals, solving the challenging subject occlusion and partassignment problems using 3D geometric reasoning, graph neural networks, and semi-supervised learning. We collected over 110 million 3D pose samples in interacting rats and mice, including seven monogenic autism rat lines. Using a multi-scale embedding approach, we identified a rich landscape of stereotyped actions, interactions, synchrony, and body contacts. This high-resolution phenotyping revealed a spectrum of changes in autism models and in response to amphetamine not resolved by conventional measurements. Our framework and large library of interactions will facilitate studies of social behaviors and their neurobiological  underpinnings.

Large language models deconstruct the clinical intuition behind diagnosing autism

Jack Stanley,1,2,6 Emmett Rabot,3,4,6 Siva Reddy,1 Eugene Belilovsky,1,5 Laurent Mottron,3,4,7 and Danilo Bzdok1,2,7,8,*

1 Mila - Que´ bec Artificial Intelligence Institute, Montre´ al, QC H2S3H1, Canada

2 The Neuro - Montre´ al Neurological Institute (MNI), McConnell Brain Imaging Centre, Department of Biomedical Engineering, Faculty of Medicine, School of Computer Science, McGill University, Montre´ al, QC H3A2B4, Canada

3 Research Center, Centre Inte´ gre´ Universitaire de Sante´ et de Services Sociaux du Nord-de-l’Ile-de-Montre´ al (CIUSSS-NIM), Montre´ al, QC H4K1B3, Canada

4 Universite´ de Montre´ al, Montre´ al, QC H3C3J7, Canada

5 Department of Computer Science and Software Engineering, Concordia University, Montreal, QC H3G 1M8, Canada

6 These authors contributed equally

7 These authors contributed equally

8 Lead contact

*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。

https://doi.org/10.1016/j.cell.2025.02.025

SUMMARY

Efforts to use genome-wide assays or brain scans to diagnose autism have seen diminishing returns. Yet the clinical intuition of healthcare professionals, based on longstanding first-hand experience, remains the gold standard for diagnosis of autism. We leveraged deep learning to deconstruct and interrogate the logic of expert clinician intuition from clinical reports to inform our understanding of autism. After pre-training on hundreds of millions of general sentences, we finessed large language models (LLMs) on >4,000 free-form health records from healthcare professionals to distinguish confirmed versus suspected autism cases. By introducing an explainability strategy, our extended language model architecture could pin down the most salient single sentences in what drives clinical thinking toward correct diagnoses. Our framework flagged the most autism-critical DSM-5 criteria to be stereotyped repetitive behaviors, special interests, and perception-based behaviors, which challenges today,s focus on deficits in social interplay, suggesting necessary revision of long-trusted diagnostic criteria in gold-standard instruments.