Publications

2023

Bryan, John P, Loïc Binan, Cai McCann, Yonina C Eldar, Samouil L Farhi, and Brian Cleary. 2023. “Optimization-Based Decoding of Imaging Spatial Transcriptomics Data”. Bioinformatics. https://doi.org/10.1093/bioinformatics/btad362.

"Imaging Spatial Transcriptomics (iST) techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods.We describe the Joint Sparse method for Imaging Transcriptomics (JSIT), an algorithm for decoding lower magnification iST data than that used in standard experimental workflows. JSIT incorporates codebook knowledge and sparsity assumptions into an optimization problem which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization (MERFISH) on tissue from mouse brain, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods.Software implementation of JSIT, together with example files, are available at https://github.com/jpbryan13/JSIT.Supplementary data are available at Bioinformatics online."

Pietiläinen, Olli, Aditi Trehan, Daniel Meyer, Jana Mitchell, Matthew Tegtmeyer, Vera Valakh, Hilena Gebre, et al. 2023. “Astrocytic cell adhesion genes linked to schizophrenia correlate with synaptic programs in neurons”. Cell Reports 42 (1): 111988. https://doi.org/https://doi.org/10.1016/j.celrep.2022.111988.
Summary The maturation of neurons and the development of synapses, although emblematic of neurons, also relies on interactions with astrocytes and other glia. Here, to study the role of glia-neuron interactions, we analyze the transcriptomes of human pluripotent stem cell (hPSC)-derived neurons, from 80 human donors, that were cultured with or without contact with glial cells. We find that the presence of astrocytes enhances synaptic gene-expression programs in neurons when in physical contact with astrocytes. These changes in neurons correlate with increased expression, in the cocultured glia, of genes that encode synaptic cell adhesion molecules. Both the neuronal and astrocyte gene-expression programs are enriched for genes associated with schizophrenia risk. Our results suggest that astrocyte-expressed genes with synaptic functions are associated with stronger expression of synaptic genetic programs in neurons, and they suggest a potential role for astrocyte-neuron interactions in schizophrenia.

2022

Stogsdill, Jeffrey A., Kwanho Kim, Loïc Binan, Samouil L. Farhi, Joshua Z. Levin, and Paola Arlotta. (2024) 2022. “Pyramidal neuron subtype diversity governs microglia states in the neocortex”. Nature 608 (7924): 750-56. https://doi.org/10.1038/s41586-022-05056-7.
Microglia are specialized macrophages in the brain parenchyma that exist in multiple transcriptional states and reside within a wide range of neuronal environments1—4. However, how and where these states are generated remains poorly understood. Here, using the mouse somatosensory cortex, we demonstrate that microglia density and molecular state acquisition are determined by the local composition of pyramidal neuron classes. Using single-cell and spatial transcriptomic profiling, we unveil the molecular signatures and spatial distributions of diverse microglia populations and show that certain states are enriched in specific cortical layers, whereas others are broadly distributed throughout the cortex. Notably, conversion of deep-layer pyramidal neurons to an alternate class identity reconfigures the distribution of local, layer-enriched homeostatic microglia to match the new neuronal niche. Leveraging the transcriptional diversity of pyramidal neurons in the neocortex, we construct a ligand—receptor atlas describing interactions between individual pyramidal neuron subtypes and microglia states, revealing rules of neuron—microglia communication. Our findings uncover a fundamental role for neuronal diversity in instructing the acquisition of microglia states as a potential mechanism for fine-tuning neuroimmune interactions within the cortical local circuitry.
Hwang, William L., Karthik A. Jagadeesh, Jimmy A. Guo, Hannah I. Hoffman, Payman Yadollahpour, Jason W. Reeves, Rahul Mohan, et al. (2024) 2022. “Single-nucleus and spatial transcriptome profiling of pancreatic cancer identifies multicellular dynamics associated with neoadjuvant treatment”. Nature Genetics 54 (8): 1178-91. https://doi.org/10.1038/s41588-022-01134-8.
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and treatment-refractory cancer. Molecular stratification in pancreatic cancer remains rudimentary and does not yet inform clinical management or therapeutic development. Here, we construct a high-resolution molecular landscape of the cellular subtypes and spatial communities that compose PDAC using single-nucleus RNA sequencing and whole-transcriptome digital spatial profiling (DSP) of 43 primary PDAC tumor specimens that either received neoadjuvant therapy or were treatment naive. We uncovered recurrent expression programs across malignant cells and fibroblasts, including a newly identified neural-like progenitor malignant cell program that was enriched after chemotherapy and radiotherapy and associated with poor prognosis in independent cohorts. Integrating spatial and cellular profiles revealed three multicellular communities with distinct contributions from malignant, fibroblast and immune subtypes: classical, squamoid-basaloid and treatment enriched. Our refined molecular and cellular taxonomy can provide a framework for stratification in clinical trials and serve as a roadmap for therapeutic targeting of specific cellular phenotypes and multicellular interactions.
Berryer, Martin H., Matthew Tegtmeyer, Loïc Binan, Vera Valakh, Anna Nathanson, Darina Trendafilova, Ethan Crouse, et al. 2022. “Robust induction of functional astrocytes using NGN2 expression in human pluripotent stem cells”. BioRxiv. https://doi.org/10.1101/2022.09.07.507028.
Astrocytes play essential roles in normal brain function, with dysfunction implicated in diverse developmental and degenerative disease processes. Emerging evidence of profound species divergent features of astrocytes coupled with the relative inaccessibility of human brain tissue underscore the utility of human pluripotent stem cell (hPSC) technologies for the generation and study of human astrocytes. However, existing approaches for hPSC-astrocyte generation are typically lengthy, incompletely characterized, or require intermediate purification steps, limiting their utility for multi-cell line, adequately powered functional studies. Here, we establish a rapid and highly scalable method for generating functional human induced astrocytes (hiAs) based upon transient Neurogenin 2 (NGN2) induction of neural progenitor-like cells followed by maturation in astrocyte media, which demonstrate remarkable homogeneity within the population and across 11 independent cell lines in the absence of additional purification steps. These hiAs express canonical astrocyte markers, respond to pro-inflammatory stimuli, exhibit ATP-induced calcium transients and support neuronal maturation in vitro. Moreover, single-cell transcriptomic analyses reveal the generation of highly reproducible cell populations across individual donors, most closely resembling human fetal astrocytes, and highly similar to hPSC-derived astrocytes generated using more complex approaches. Finally, the hiAs capture key molecular hallmarks in a trisomy 21 disease model. Thus, hiAs provide a valuable and practical resource well-suited for study of basic human astrocyte function and dysfunction in disease.Competing Interest StatementL.L.R. is a founder of Elevian, Rejuveron, and Vesalius Therapeutics, a member of their scientific advisory boards and a private equity shareholder. All are interested in formulating approaches intended to treat diseases of the nervous system and other tissues. He is also on the advisory board of Alkahest, a Grifols company, focused on the plasma proteome and brain aging. None of these companies provided any financial support for the work in this paper. The remaining authors declare no competing interests.
Bryan, John P., Loïc Binan, Cai McCann, Yonina C. Eldar, Samouil L. Farhi, and Brian Cleary. 2022. “Optimization-Based Decoding of Imaging Spatial Transcriptomics Data”. BioRxiv. https://doi.org/10.1101/2022.11.22.517523.
Motivation: Imaging Spatial Transcriptomics (iST) techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods. Results: We describe the Joint Sparse method for Imaging Transcriptomics (JSIT), an algorithm for decoding lower magnification IT data than that used in standard experimental workflows. JSIT incorporates codebook knowledge and sparsity assumptions into an optimization problem which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization (MERFISH) on tissue from mouse motor cortex, we demonstrate that JSIT enables improved throughput and recovery performance over standard decoding methods.Competing Interest StatementThe authors have declared no competing interest.
Nehme, Ralda, Olli Pietiläinen, Mykyta Artomov, Matthew Tegtmeyer, Vera Valakh, Leevi Lehtonen, Christina Bell, et al. (2024) 2022. “The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia”. Nature Communications 13 (1): 3690. https://doi.org/10.1038/s41467-022-31436-8.
It is unclear how the 22q11.2 deletion predisposes to psychiatric disease. To study this, we generated induced pluripotent stem cells from deletion carriers and controls and utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Here, we show that upon differentiation into neural progenitor cells, the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including autism. In excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common and rare variants. To understand how the deletion contributed to these changes, we defined the minimal protein-protein interaction network that best explains gene expression alterations. We found that many genes in 22q11.2 interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways. Our findings suggest that the 22q11.2 deletion impacts genes that may converge with psychiatric risk loci to influence disease manifestation in each deletion carrier.
Schapiro, Denis, Clarence Yapp, Artem Sokolov, Sheila M. Reynolds, Yu-An Chen, Damir Sudar, Yubin Xie, et al. (2024) 2022. “MITI minimum information guidelines for highly multiplexed tissue images”. Nature Methods 19 (3): 262-67. https://doi.org/10.1038/s41592-022-01415-4.
The imminent release of tissue atlases combining multichannel microscopy with single-cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards to guide data deposition, curation and release. We describe a Minimum Information about Highly Multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and for other microscopy data to highly multiplexed tissue images and traditional histology.
Schapiro, Denis, Artem Sokolov, Clarence Yapp, Yu-An Chen, Jeremy L. Muhlich, Joshua Hess, Allison L. Creason, et al. (2024) 2022. “MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging”. Nature Methods 19 (3): 311-15. https://doi.org/10.1038/s41592-021-01308-y.
Highly multiplexed tissue imaging makes detailed molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of large multichannel images poses a substantial computational challenge. Here, we describe a modular and open-source computational pipeline, MCMICRO, for performing the sequential steps needed to transform whole-slide images into single-cell data. We demonstrate the use of MCMICRO on tissue and tumor images acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.