[ { "id": "authors:c2z5m-pmm02", "collection": "authors", "collection_id": "c2z5m-pmm02", "cite_using_url": "https://authors.library.caltech.edu/records/c2z5m-pmm02", "type": "monograph", "title": "Comparative analysis of multiplex single-cell mRNA sequencing of resting and activated PBMCs using droplet-based and split-pool methods", "author": [ { "family_name": "Zhang", "given_name": "An", "clpid": "Zhang-An" }, { "family_name": "Yue", "given_name": "Jiahe Verona", "clpid": "Yue-Jiahe-Verona" }, { "family_name": "Ettlin", "given_name": "Olivia", "orcid": "0000-0002-9243-9752", "clpid": "Ettlin-Olivia" }, { "family_name": "Blanco", "given_name": "Mario", "orcid": "0000-0002-9852-2231", "clpid": "Blanco-Mario-R" }, { "family_name": "Chen", "given_name": "Yu-Jen", "clpid": "Chen-Yu-Jen" }, { "family_name": "Liu", "given_name": "Shichen", "orcid": "0000-0002-0964-6559", "clpid": "Liu-Shichen" }, { "family_name": "Sullivan", "given_name": "Delaney", "orcid": "0000-0002-8359-6705", "clpid": "Sullivan-Delaney-Kalcey" }, { "family_name": "Shao", "given_name": "Binglun", "orcid": "0000-0002-9464-3255", "clpid": "Shao-Binglun" }, { "family_name": "Boktor", "given_name": "Joe", "orcid": "0000-0003-2456-1913", "clpid": "Boktor-Joe" }, { "family_name": "Williams", "given_name": "Brian A.", "orcid": "0000-0003-3253-611X", "clpid": "Williams-Brian-A" }, { "family_name": "Wojtowicz", "given_name": "Woj M.", "clpid": "Wojtowicz-Woj-M" }, { "family_name": "Guttman", "given_name": "Mitchell", "orcid": "0000-0003-4748-9352", "clpid": "Guttman-M" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Zinn", "given_name": "Kai", "orcid": "0000-0002-6706-5605", "clpid": "Zinn-K" } ], "abstract": "

Single-cell mRNA sequencing is an essential technology for transcriptional profiling of cells and tissues. To compare transcriptomes among samples, it is cost-effective to multiplex their processing. Multiplexing is done by barcoding cDNA copies of transcripts from each sample, then combining them into a single library. We performed multiplex sequencing of human peripheral blood mononuclear cells (PBMCs) under three different experimental conditions: resting, treated with T cell activator, and treated with TNF-a. We generated libraries using two split-pool barcode ligation methods: Parse and a new in-house split-pool method, SWIFT-seq, developed by the Guttman group. PBMCs were fixed and permeabilized using a Parse kit for the Parse and SWIFT1 libraries. In a second experiment, we fixed and permeabilized cells using
a new in-house protocol and processed samples using SWIFT-seq to generate the SWIFT2 library. We also processed live cells using the Multiseq method for 10X Genomics-based sequencing, in which cells are indexed with lipid-linked barcodes, to generate the 10X library. These libraries encompass all major PBMC cell types, but myeloid cells were overrepresented in Parse and SWIFT1, probably due to the use of the Parse kit for fixation and permeabilization. Analysis of transcriptomes defined by these libraries shows that all sequencing and analysis methods generate remarkably similar biological conclusions. The Spearman rank correlation coefficients for comparisons of marker gene expression among all methods are >0.7 for all major PBMC cell types in resting and activated populations. The same gene programs are
implicated by the four methods as being involved in transitions from resting to activated states. Our results show that 10X, Parse, and SWIFT-seq methods can be used interchangeably for multiplex sequencing of PBMCs. We estimate that the per cell cost of analysis with SWIFT-seq, which uses reagents purchased individually from suppliers, is about one-third of that for the other methods.

", "doi": "10.7907/c2z5m-pmm02", "publisher": "California Institute of Technology", "publication_date": "2025-07-15" }, { "id": "authors:e288m-hdm97", "collection": "authors", "collection_id": "e288m-hdm97", "cite_using_url": "https://authors.library.caltech.edu/records/e288m-hdm97", "type": "monograph", "title": "Automated construction of cognitive maps with predictive coding", "author": [ { "family_name": "Gornet", "given_name": "James A.", "orcid": "0000-0002-5431-7340", "clpid": "Gornet-James-A" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "

Humans construct internal cognitive maps of their environment directly from sensory inputs without access to a system of explicit coordinates or distance measurements. While machine learning algorithms like SLAM utilize specialized inference procedures to identify visual features and construct spatial maps from visual and odometry data, the general nature of cognitive maps in the brain suggests a unified mapping algorithmic strategy that can generalize to auditory, tactile, and linguistic inputs. Here, we demonstrate that predictive coding provides a natural and versatile neural network algorithm for constructing spatial maps using sensory data. We introduce a framework in which an agent navigates a virtual environment while engaging in visual predictive coding using a self-attention-equipped convolutional neural network. While learning a next image prediction task, the agent automatically constructs an internal representation of the environment that quantitatively reflects spatial distances. The internal map enables the agent to pinpoint its location relative to landmarks using only visual information.The predictive coding network generates a vectorized encoding of the environment that supports vector navigation where individual latent space units delineate localized, overlapping neighborhoods in the environment. Broadly, our work introduces predictive coding as a unified algorithmic framework for constructing cognitive maps that can naturally extend to the mapping of auditory, sensorimotor, and linguistic inputs.

", "doi": "10.1101/2023.09.18.558369", "issn": "2692-8205", "publisher": "Cold Spring Harbor Laboratory Press", "publication": "bioRxiv", "publication_date": "2024-04-18", "pages": "2023.09.18.558369" }, { "id": "authors:sv6fh-01002", "collection": "authors", "collection_id": "sv6fh-01002", "cite_using_url": "https://authors.library.caltech.edu/records/sv6fh-01002", "type": "monograph", "title": "Unexplored regions of the protein sequence-structure map revealed at scale by a library of foldtuned language models", "author": [ { "family_name": "Subramanian", "given_name": "Arjuna M.", "orcid": "0009-0004-2790-0209", "clpid": "Subramanian-Arjuna-M" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "
\n

Nature has likely sampled only a fraction of all protein sequences and structures allowed by the laws of biophysics. However, the combinatorial scale of amino-acid sequence-space has traditionally precluded substantive study of the full protein sequence-structure map. In particular, it remains unknown how much of the vast uncharted landscape of far-from-natural sequences consists of alternate ways to encode the familiar ensemble of natural folds; proteins in this category also represent an opportunity to diversify candidates for downstream applications. Here, we characterize sequence-structure mapping in far-from-natural regions of sequence-space guided by the capacity of protein language models (pLMs) to explore sequences outside their natural training data through generation. We demonstrate that pre-trained generative pLMs sample a limited structural snapshot of the natural protein universe, including >350 common (sub)domain elements. Incorporating pLM, structure prediction, and structure-based search techniques, we surpass this limitation by developing a novel “foldtuning” strategy that pushes a pretrained pLM into a generative regime that maintains structural similarity to a target protein fold (e.g. TIM barrel, thioredoxin, etc) while maximizing dissimilarity to natural amino-acid sequences. We apply “foldtuning” to build a library of pLMs for >700 naturally-abundant folds in the SCOP database, accessing swaths of proteins that take familiar structures yet lie far from known sequences, spanning targets that include enzymes, immune ligands, and signaling proteins. By revealing protein sequence-structure information at scale outside of the context of evolution, we anticipate that this work will enable future systematic searches for wholly novel folds and facilitate more immediate protein design goals in catalysis and medicine.

\n
", "doi": "10.1101/2023.12.22.573145", "pmcid": "PMC10769378", "publisher": "Cold Spring Harbor Laboratory Press", "publication_date": "2023-12-23" }, { "id": "authors:hx122-ds441", "collection": "authors", "collection_id": "hx122-ds441", "cite_using_url": "https://authors.library.caltech.edu/records/hx122-ds441", "type": "monograph", "title": "Spatial transcriptomics defines injury-specific microenvironments in the adult mouse kidney and novel cellular interactions in regeneration and disease", "author": [ { "family_name": "Polonsky", "given_name": "Michal", "orcid": "0000-0003-3871-460X", "clpid": "Polonsky-Michal" }, { "family_name": "Gerhardt", "given_name": "Louisa M. S.", "orcid": "0000-0003-3052-1563", "clpid": "Gerhardt-Louisa-M-S" }, { "family_name": "Yun", "given_name": "Jina", "clpid": "Yun-Jina" }, { "family_name": "Koppitch", "given_name": "Kari", "clpid": "Koppitch-Kari" }, { "family_name": "Col\u00f3n", "given_name": "Katsuya Lex", "orcid": "0000-0002-7347-6128", "clpid": "Col\u00f3n-Katsuya-Lex" }, { "family_name": "Amrhein", "given_name": "Henry", "orcid": "0000-0002-4264-140X", "clpid": "Amrhein-H" }, { "family_name": "Zheng", "given_name": "Shiwei", "orcid": "0000-0001-6682-6743", "clpid": "Zheng-Shiwei" }, { "family_name": "Yuan", "given_name": "Guo-Cheng", "clpid": "Yuan-Guo-Cheng" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Cai", "given_name": "Long", "orcid": "0000-0002-7154-5361", "clpid": "Cai-Long" }, { "family_name": "McMahon", "given_name": "Andrew P.", "orcid": "0000-0002-3779-1729", "clpid": "McMahon-Andrew-P" } ], "abstract": "

Kidney injury disrupts the intricate renal architecture and triggers limited regeneration, and injury-invoked inflammation and fibrosis. Deciphering molecular pathways and cellular interactions driving these processes is challenging due to the complex renal architecture. Here, we applied single cell spatial transcriptomics to examine ischemia-reperfusion injury in the mouse kidney. Spatial transcriptomics revealed injury-specific and spatially-dependent gene expression patterns in distinct cellular microenvironments within the kidney and predicted Clcf1-Crfl1 in a molecular interplay between persistently injured proximal tubule cells and neighboring fibroblasts. Immune cell types play a critical role in organ repair. Spatial analysis revealed cellular microenvironments resembling early tertiary lymphoid structures and identified associated molecular pathways. Collectively, this study supports a focus on molecular interactions in cellular microenvironments to enhance understanding of injury, repair and disease.

", "doi": "10.1101/2023.11.22.568217", "issn": "2692-8205", "publisher": "BioRxiv", "publication": "bioRxiv", "publication_date": "2023-11-22" }, { "id": "authors:0p6md-nzv84", "collection": "authors", "collection_id": "0p6md-nzv84", "cite_using_url": "https://authors.library.caltech.edu/records/0p6md-nzv84", "type": "monograph", "title": "TRILL: Orchestrating Modular Deep-Learning Workflows for Democratized, Scalable Protein Analysis and Engineering", "author": [ { "family_name": "Martinez", "given_name": "Zachary A", "orcid": "0000-0002-7830-3162", "clpid": "Martinez-Zachary-A" }, { "family_name": "Murray", "given_name": "Richard M.", "orcid": "0000-0002-5785-7481", "clpid": "Murray-R-M" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "

Deep-learning models have been rapidly adopted by many fields, partly due to the deluge of data humanity has amassed. In particular, the petabases of biological sequencing data enable the unsupervised training of protein language models that learn the “language of life.” However, due to their prohibitive size and complexity, contemporary deep-learning models are often unwieldy, especially for scientists with limited machine learning backgrounds. TRILL (TRaining and Inference using the Language of Life) is a platform for creative protein design and discovery. Leveraging several state-of-the-art models such as ESM-2, DiffDock, and RFDiffusion, TRILL allows researchers to generate novel proteins, predict 3-D structures, extract high-dimensional representations of proteins, functionally classify proteins and more. What sets TRILL apart is its ability to enable complex pipelines by chaining together models and effectively merging the capabilities of different models to achieve a sum greater than its individual parts. Whether using Google Colab with one GPU or a supercomputer with hundreds, TRILL allows scientists to effectively utilize models with millions to billions of parameters by using optimized training strategies such as ZeRO-Offload and distributed data parallel. Therefore, TRILL not only bridges the gap between complex deep-learning models and their practical application in the field of biology, but also simplifies the orchestration of these models into comprehensive workflows, democratizing access to powerful methods. Documentation: https://trill.readthedocs.io/en/latest/home.html.

", "doi": "10.1101/2023.10.24.563881", "pmcid": "PMC10659302", "issn": "2692-8205", "publisher": "Cold Spring Harbor Laboratory Press", "publication": "bioRxiv", "publication_date": "2023-11-10", "pages": "2023.10.24.563881" }, { "id": "authors:7wsj9-9v221", "collection": "authors", "collection_id": "7wsj9-9v221", "cite_using_url": "https://authors.library.caltech.edu/records/7wsj9-9v221", "type": "monograph", "title": "Configurational entropy is an intrinsic driver of tissue structural heterogeneity", "author": [ { "family_name": "Srivastava", "given_name": "Vasudha", "orcid": "0000-0001-8845-9518", "clpid": "Srivastava-Vasudha" }, { "family_name": "Hu", "given_name": "Jennifer L.", "clpid": "Hu-Jennifer-L" }, { "family_name": "Garbe", "given_name": "James C.", "orcid": "0000-0002-4041-3868", "clpid": "Garbe-James-C" }, { "family_name": "Veytsman", "given_name": "Boris", "orcid": "0000-0003-4674-8113", "clpid": "Veytsman-Boris" }, { "family_name": "Shalabi", "given_name": "Sundus F.", "orcid": "0000-0002-8440-2474", "clpid": "Shalabi-Sundus-F" }, { "family_name": "Yllanes", "given_name": "David", "orcid": "0000-0001-7276-2942", "clpid": "Yllanes-David" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "LaBarge", "given_name": "Mark A.", "orcid": "0000-0003-2405-4719", "clpid": "LaBarge-Mark-A" }, { "family_name": "Huber", "given_name": "Greg", "orcid": "0000-0002-8292-4164", "clpid": "Huber-Greg" }, { "family_name": "Gartner", "given_name": "Zev J.", "orcid": "0000-0001-7803-1219", "clpid": "Gartner-Zev-J" } ], "abstract": "
\n

Tissues comprise ordered arrangements of cells that can be surprisingly disordered in their details. How the properties of single cells and their microenvironment contribute to the balance between order and disorder at the tissue-scale remains poorly understood. Here, we address this question using the self-organization of human mammary organoids as a model. We find that organoids behave like a dynamic structural ensemble at the steady state. We apply a maximum entropy formalism to derive the ensemble distribution from three measurable parameters – the degeneracy of structural states, interfacial energy, and tissue activity (the energy associated with positional fluctuations). We link these parameters with the molecular and microenvironmental factors that control them to precisely engineer the ensemble across multiple conditions. Our analysis reveals that the entropy associated with structural degeneracy sets a theoretical limit to tissue order and provides new insight for tissue engineering, development, and our understanding of disease progression.

\n
", "doi": "10.1101/2023.07.01.546933", "pmcid": "PMC10327153", "publisher": "Cold Spring Harbor Laboratory Press", "publication_date": "2023-07-02" }, { "id": "authors:zpnpe-wnj98", "collection": "authors", "collection_id": "zpnpe-wnj98", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20230628-257115000.23", "type": "monograph", "title": "D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response", "author": [ { "family_name": "Jiang", "given_name": "Jialong", "clpid": "Jiang-Jialong" }, { "family_name": "Chen", "given_name": "Sisi", "orcid": "0000-0001-9448-9713", "clpid": "Chen-Sisi" }, { "family_name": "Tsou", "given_name": "Tiffany", "orcid": "0000-0002-5651-2879", "clpid": "Tsou-Tiffany" }, { "family_name": "McGinnis", "given_name": "Christopher S.", "orcid": "0000-0001-6923-9341", "clpid": "McGinnis-Christopher-S" }, { "family_name": "Khazaei", "given_name": "Tahmineh", "orcid": "0000-0002-4743-2383", "clpid": "Khazaei-Tahmineh" }, { "family_name": "Zhu", "given_name": "Qin", "orcid": "0000-0001-5539-6071", "clpid": "Zhu-Qin" }, { "family_name": "Park", "given_name": "Jong H.", "clpid": "Park-Jong-H" }, { "family_name": "Strazhnik", "given_name": "Inna-Marie", "orcid": "0000-0002-6929-9456", "clpid": "Strazhnik-Inna-Marie" }, { "family_name": "Hanna", "given_name": "John", "clpid": "Hanna-John" }, { "family_name": "Chow", "given_name": "Eric D.", "orcid": "0000-0001-8079-918X", "clpid": "Chow-Eric-D" }, { "family_name": "Sivak", "given_name": "David A.", "orcid": "0000-0003-4815-4722", "clpid": "Sivak-David-A" }, { "family_name": "Gartner", "given_name": "Zev J.", "orcid": "0000-0001-7803-1219", "clpid": "Gartner-Zev-J" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of generegulatory networks from single-cell mRNA-seq data sets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.", "doi": "10.1101/2023.04.19.537364", "pmcid": "PMC10153191", "publication_date": "2023-04-21" }, { "id": "authors:b6q9d-jgb37", "collection": "authors", "collection_id": "b6q9d-jgb37", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20230322-366904000.5", "type": "monograph", "title": "Control of spatio-temporal patterning via cell density in a multicellular synthetic gene circuit", "author": [ { "family_name": "Santorelli", "given_name": "Marco", "orcid": "0000-0002-9633-7825", "clpid": "Santorelli-Marco" }, { "family_name": "Bhamidipati", "given_name": "Pranav S.", "orcid": "0000-0002-6199-6505", "clpid": "Bhamidipati-Pranav-S" }, { "family_name": "Kavanagh", "given_name": "Andriu", "clpid": "Kavanagh-Andriu" }, { "family_name": "MacKrell", "given_name": "Victoria A.", "clpid": "MacKrell-Victoria-A" }, { "family_name": "Sondkar", "given_name": "Trusha", "clpid": "Sondkar-Trusha" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Morsut", "given_name": "Leonardo", "orcid": "0000-0001-7049-3478", "clpid": "Morsut-Leonardo" } ], "abstract": "A major goal in synthetic development is to design and construct gene regulatory circuits that control the patterning and morphogenesis of synthetic multicellular structures. In natural development, an interplay between mechanical and chemical communication shapes the dynamics of gene regulatory circuits that underlie patterning and morphogenesis. However, for synthetic gene circuits, how the non-genetic properties of the growth environment impact circuit behavior remains poorly understood. Here, we describe an occurrence of mechano-chemical coupling in synthetic contact-dependent synNotch patterning circuits demonstrating that cell density modulates the transduction of signal between a sender and receiver cell. By exploiting density-dependent signaling, we construct multicellular signal propagation circuits with synNotch and control the patterning outcome both temporally and spatially via cell density gradients established in vitro via plating or small-molecule mediated modulation of proliferation. Our work demonstrates that synthetic gene circuits can be critically impacted by their context, providing an alternate means for programming multi-cellular circuit patterning outcomes.", "doi": "10.1101/2022.10.04.510900", "publication_date": "2022-10-06" }, { "id": "authors:x0cjg-kxg25", "collection": "authors", "collection_id": "x0cjg-kxg25", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220720-917093000", "type": "monograph", "title": "Why Daily SARS-CoV-2 Nasal Rapid Antigen Testing Poorly Detects Infected and Infectious Individuals", "author": [ { "family_name": "Winnett", "given_name": "Alexander Viloria", "orcid": "0000-0002-7338-5605", "clpid": "Winnett-Alexander-Viloria" }, { "family_name": "Akana", "given_name": "Reid", "orcid": "0000-0003-4422-587X", "clpid": "Akana-Reid" }, { "family_name": "Shelby", "given_name": "Natasha", "orcid": "0000-0001-9097-3663", "clpid": "Shelby-Natasha" }, { "family_name": "Davich", "given_name": "Hannah", "orcid": "0000-0001-6880-3086", "clpid": "Davich-Hannah" }, { "family_name": "Caldera", "given_name": "Saharai", "orcid": "0000-0001-5057-9186", "clpid": "Caldera-Saharai" }, { "family_name": "Yamada", "given_name": "Taikun", "clpid": "Yamada-Taikun" }, { "family_name": "Reyna", "given_name": "John Raymond B.", "clpid": "Reyna-John-Raymond-B" }, { "family_name": "Romano", "given_name": "Anna E.", "orcid": "0000-0002-7148-0668", "clpid": "Romano-Anne-E" }, { "family_name": "Carter", "given_name": "Alyssa M.", "orcid": "0000-0002-2776-9421", "clpid": "Carter-Alyssa-M" }, { "family_name": "Kim", "given_name": "Mi Kyung", "clpid": "Kim-Mi-Kyung" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Tognazzini", "given_name": "Colten", "orcid": "0000-0002-2754-3588", "clpid": "Tognazzini-Colten" }, { "family_name": "Feaster", "given_name": "Matthew", "orcid": "0000-0001-9966-2845", "clpid": "Feaster-Matthew" }, { "family_name": "Goh", "given_name": "Ying-Ying", "orcid": "0000-0001-5136-7214", "clpid": "Goh-Ying-Ying" }, { "family_name": "Chew", "given_name": "Yap Ching", "orcid": "0000-0002-1686-6541", "clpid": "Chew-Yap-Ching" }, { "family_name": "Ismagilov", "given_name": "Rustem F.", "orcid": "0000-0002-3680-4399", "clpid": "Ismagilov-R-F" } ], "abstract": "Background. In a recent household-transmission study of SARS-CoV-2, we found extreme differences in SARS-CoV-2 viral loads among paired saliva, anterior-nares swab (ANS) and oropharyngeal swab specimens collected from the same timepoint. We hypothesized these differences may hinder low-analytical-sensitivity assays (including antigen rapid diagnostic tests, Ag-RDTs) using a single specimen type (e.g., ANS) from reliably detecting infected and infectious individuals. \n\nMethods. We evaluated a daily at-home ANS Ag-RDT (Quidel QuickVue) in a cross-sectional analysis of 228 individuals and in a longitudinal analysis (throughout infection) of 17 individuals enrolled early in the course of infection. Ag-RDT results were compared to RT-qPCR results and high, presumably infectious viral loads (in each, or any, specimen type). \n\nResults. The ANS Ag-RDT correctly detected only 44% of timepoints from infected individuals on cross-sectional analysis, and in this population had an inferred limit of detection of 7.6 \u00d7 10\u2076 copies/mL. From the longitudinal cohort, daily Ag-RDT clinical sensitivity was very low (<3%) during the early, pre-infectious period of the infection. Further, the Ag-RDT detected \u226463% of presumably infectious timepoints. The poor observed clinical sensitivity of the Ag-RDT was similar to what was predicted based on quantitative ANS viral loads and the inferred limit of detection of the ANS Ag-RDT being evaluated, indicating high-quality self-sampling. \n\nConclusion. Nasal Ag-RDTs, even when used daily, can miss individuals infected with the Omicron variant and even those presumably infectious. Evaluations of Ag-RDT detection of infected or infectious individuals should be compared with a composite (multi-specimen) infection status to correctly assess performance. \n\nKey points. Nasal-swab rapid antigen tests have low analytical sensitivity and the sampling of only the nasal cavity hinders their ability to detect infected individuals, including those with high and presumably infectious viral loads in throat or saliva specimens.", "doi": "10.1101/2022.07.13.22277513", "publication_date": "2022-07-15" }, { "id": "authors:vtef0-x7037", "collection": "authors", "collection_id": "vtef0-x7037", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220720-917402000", "type": "monograph", "title": "Extreme differences in SARS-CoV-2 viral loads among respiratory specimen types during presumed pre-infectious and infectious periods", "author": [ { "family_name": "Winnett", "given_name": "Alexander Viloria", "orcid": "0000-0002-7338-5605", "clpid": "Winnett-Alexander-V" }, { "family_name": "Akana", "given_name": "Reid", "orcid": "0000-0003-4422-587X", "clpid": "Akana-Reid" }, { "family_name": "Shelby", "given_name": "Natasha", "orcid": "0000-0001-9097-3663", "clpid": "Shelby-Natasha" }, { "family_name": "Davich", "given_name": "Hannah", "orcid": "0000-0001-6880-3086", "clpid": "Davich-Hannah" }, { "family_name": "Caldera", "given_name": "Saharai", "orcid": "0000-0001-5057-9186", "clpid": "Caldera-Saharai" }, { "family_name": "Yamada", "given_name": "Taikun", "clpid": "Yamada-Taikun" }, { "family_name": "Reyna", "given_name": "John Raymond B.", "clpid": "Reyna-John-Raymond-B" }, { "family_name": "Romano", "given_name": "Anna E.", "orcid": "0000-0002-7148-0668", "clpid": "Romano-Anne-E" }, { "family_name": "Carter", "given_name": "Alyssa M.", "orcid": "0000-0002-2776-9421", "clpid": "Carter-Alyssa-M" }, { "family_name": "Kim", "given_name": "Mi Kyung", "clpid": "Kim-Mi-Kyung" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Tognazzini", "given_name": "Colten", "orcid": "0000-0002-2754-3588", "clpid": "Tognazzini-Colten" }, { "family_name": "Feaster", "given_name": "Matthew", "orcid": "0000-0001-9966-2845", "clpid": "Feaster-Matthew" }, { "family_name": "Goh", "given_name": "Ying-Ying", "orcid": "0000-0001-5136-7214", "clpid": "Goh-Ying-Ying" }, { "family_name": "Chew", "given_name": "Yap Ching", "orcid": "0000-0002-1686-6541", "clpid": "Chew-Yap-Ching" }, { "family_name": "Ismagilov", "given_name": "Rustem F.", "orcid": "0000-0002-3680-4399", "clpid": "Ismagilov-R-F" } ], "abstract": "SARS-CoV-2 viral load measurements from a single specimen type are used to establish diagnostic strategies, interpret clinical-trial results for vaccines and therapeutics, model viral transmission, and understand virus-host interactions. However, measurements from a single specimen type are implicitly assumed to be representative of other specimen types. We quantified viral-load timecourses from individuals who began daily self-sampling of saliva, anterior nares (nasal), and oropharyngeal (throat) swabs before or at the incidence of infection with the Omicron variant. Viral loads in different specimen types from the same person at the same timepoint exhibited extreme differences, up to 109 copies/mL. These differences were not due to variation in sample self-collection, which was consistent. For most individuals, longitudinal viral-load timecourses in different specimen types did not correlate. Throat-swab and saliva viral loads began to rise up to 7 days earlier than nasal-swab viral loads in most individuals, leading to very low clinical sensitivity of nasal swabs during the first days of infection. Individuals frequently exhibited presumably infectious viral loads in one specimen type while viral loads were low or undetectable in other specimen types. Therefore, defining an individual as infectious based on assessment of a single specimen type underestimates the infectious period, and overestimates the ability of that specimen type to detect infectious individuals. For diagnostic COVID-19 testing, these three single specimen types have low clinical sensitivity, whereas a combined throat-nasal swab, and assays with high analytical sensitivity, were inferred to have significantly better clinical sensitivity to detect presumed pre-infectious and infectious individuals.", "doi": "10.1101/2022.07.13.22277113", "publication_date": "2022-07-15" }, { "id": "authors:6vtvn-09279", "collection": "authors", "collection_id": "6vtvn-09279", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220706-965670000", "type": "monograph", "title": "A prebiotic diet modulates microglial states and motor deficits in \u03b1-synuclein overexpressing mice", "author": [ { "family_name": "Abdel-Haq", "given_name": "Reem", "orcid": "0000-0002-7418-5736", "clpid": "Abdel-Haq-Reem" }, { "family_name": "Schlachetzki", "given_name": "Johannes C. M.", "orcid": "0000-0002-7801-9743", "clpid": "Schlachetzki-Johannes-C-M" }, { "family_name": "Boktor", "given_name": "Joseph C.", "orcid": "0000-0003-2456-1913", "clpid": "Boktor-Joseph-C" }, { "family_name": "Cantu-Jungles", "given_name": "Thaisa M.", "orcid": "0000-0001-8928-9717", "clpid": "Cantu-Jungles-Thaisa-M" }, { "family_name": "Thron", "given_name": "Taren", "orcid": "0000-0001-9577-2617", "clpid": "Thron-Taren-M" }, { "family_name": "Zhang", "given_name": "Mengying", "clpid": "Zhang-Mengying" }, { "family_name": "Bostick", "given_name": "John W.", "orcid": "0000-0001-8925-2447", "clpid": "Bostick-John-W" }, { "family_name": "Khazaei", "given_name": "Tahmineh", "orcid": "0000-0002-4743-2383", "clpid": "Khazaei-Tahmineh" }, { "family_name": "Chilakala", "given_name": "Sujatha", "orcid": "0000-0003-1581-3381", "clpid": "Chilakala-Sujatha" }, { "family_name": "Morais", "given_name": "Livia H.", "orcid": "0000-0002-5738-2658", "clpid": "Morais-Livia-H" }, { "family_name": "Humphrey", "given_name": "Greg", "clpid": "Humphrey-Gregory" }, { "family_name": "Keshavarzian", "given_name": "Ali", "orcid": "0000-0002-7969-3369", "clpid": "Keshavarzian-Ali" }, { "family_name": "Katz", "given_name": "Jonathan E.", "clpid": "Katz-Jonathan-E" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Knight", "given_name": "Rob", "orcid": "0000-0002-0975-9019", "clpid": "Knight-Rob" }, { "family_name": "Gradinaru", "given_name": "Viviana", "orcid": "0000-0001-5868-348X", "clpid": "Gradinaru-V" }, { "family_name": "Hamaker", "given_name": "Bruce R.", "orcid": "0000-0001-6591-942X", "clpid": "Hamaker-Bruce-R" }, { "family_name": "Glass", "given_name": "Christopher K.", "orcid": "0000-0003-4344-3592", "clpid": "Glass-Christopher-K" }, { "family_name": "Mazmanian", "given_name": "Sarkis K.", "orcid": "0000-0003-2713-1513", "clpid": "Mazmanian-S-K" } ], "abstract": "Parkinson's disease (PD) is a movement disorder characterized by neuroinflammation, \u03b1-synuclein pathology, and neurodegeneration. Most cases of PD are non-hereditary, suggesting a strong role for environmental factors, and it has been speculated that disease may originate in peripheral tissues such as the gastrointestinal (GI) tract before affecting the brain. The gut microbiome is altered in PD and may impact motor and GI symptoms as indicated by animal studies, though mechanisms of gut-brain interactions remain incompletely defined. Intestinal bacteria ferment dietary fibers into short-chain fatty acids, with fecal levels of these molecules differing between PD and healthy controls and in mouse models. Among other effects, dietary microbial metabolites can modulate activation of microglia, brain-resident immune cells implicated in PD. We therefore investigated whether a fiber-rich diet influences microglial function in \u03b1-synuclein overexpressing (ASO) mice, a preclinical model with PD-like symptoms and pathology. Feeding a prebiotic high-fiber diet attenuates motor deficits and reduces \u03b1-synuclein aggregation in the substantia nigra of mice. Concomitantly, the gut microbiome of ASO mice adopts a profile correlated with health upon prebiotic treatment, which also reduces microglial activation. Single-cell RNA-seq analysis of microglia from the substantia nigra and striatum uncovers increased pro-inflammatory signaling and reduced homeostatic responses in ASO mice compared to wild-type counterparts on standard diets. However, prebiotic feeding reverses pathogenic microglial states in ASO mice and promotes expansion of protective disease-associated macrophage (DAM) subsets of microglia. Notably, depletion of microglia using a CSF1R inhibitor eliminates the beneficial effects of prebiotics by restoring motor deficits to ASO mice despite feeding a prebiotic diet. These studies uncover a novel microglia-dependent interaction between diet and motor symptoms in mice, findings that may have implications for neuroinflammation and PD.", "doi": "10.1101/2022.06.27.497828", "publication_date": "2022-07-01" }, { "id": "authors:xw3ta-w7e10", "collection": "authors", "collection_id": "xw3ta-w7e10", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220816-220025879", "type": "monograph", "title": "Engineering flexible machine learning systems by traversing functionally invariant paths in weight space", "author": [ { "family_name": "Raghavan", "given_name": "Guruprasad", "orcid": "0000-0002-1970-9963", "clpid": "Raghavan-Guruprasad" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Deep neural networks achieve human-like performance on a variety of perceptual and decision-making tasks. However, networks perform poorly when confronted with changing tasks or goals, and broadly fail to match the flexibility and robustness of human intelligence. Here, we develop a mathematical and algorithmic framework that enables flexible and continuous training of neural networks on a range of objectives by constructing path connected sets of networks that achieve equivalent functional performance on a given machine learning task. We view the weight space of a neural network as a curved Riemannian manifold and move a network along a functionally invariant path in weight space while searching for networks that satisfy secondary objectives. A path-sampling algorithm trains computer vision and natural language processing networks with millions of weight parameters to learn a series of classification tasks without performance loss while accommodating secondary objectives including network sparsification, incremental task learning, and increased adversarial robustness. Broadly, we conceptualize a neural network as a mathematical object that can be iteratively transformed into distinct configurations by the path-sampling algorithm to define a sub-manifold of networks that can be harnessed to achieve user goals.", "doi": "10.48550/arXiv.2205.00334", "publisher": "arXiv", "publication_date": "2022-04-30" }, { "id": "authors:td4tm-23081", "collection": "authors", "collection_id": "td4tm-23081", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220505-806027100", "type": "monograph", "title": "Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data", "author": [ { "family_name": "Pool", "given_name": "Allan-Hermann", "orcid": "0000-0002-0811-9861", "clpid": "Pool-Allan-Hermann" }, { "family_name": "Poldsam", "given_name": "Helen", "clpid": "Poldsam-Helen" }, { "family_name": "Chen", "given_name": "Sisi", "orcid": "0000-0001-9448-9713", "clpid": "Chen-Sisi" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Oka", "given_name": "Yuki", "orcid": "0000-0003-2686-0677", "clpid": "Oka-Yuki" } ], "abstract": "Droplet-based 3' single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3' scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3' to known gene boundaries due to poor 3' UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3' gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.", "doi": "10.1101/2022.04.26.489449", "publication_date": "2022-04-29" }, { "id": "authors:4cdt6-s8y82", "collection": "authors", "collection_id": "4cdt6-s8y82", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20211102-171349069", "type": "monograph", "title": "Motor processivity and speed determine structure and dynamics of microtubule-motor assemblies", "author": [ { "family_name": "Banks", "given_name": "Rachel A.", "orcid": "0000-0003-2028-2925", "clpid": "Banks-Rachel-A" }, { "family_name": "Galstyan", "given_name": "Vahe", "orcid": "0000-0001-7073-9175", "clpid": "Galstyan-Vahe" }, { "family_name": "Lee", "given_name": "Heun Jin", "clpid": "Lee-Heun-Jin" }, { "family_name": "Hirokawa", "given_name": "Soichi", "clpid": "Hirokaw-Soichi" }, { "family_name": "Ierokomos", "given_name": "Athena", "clpid": "Ierokomos-Athena" }, { "family_name": "Ross", "given_name": "Tyler D.", "orcid": "0000-0002-7872-3992", "clpid": "Ross-Tyler-D" }, { "family_name": "Bryant", "given_name": "Zev", "orcid": "0000-0001-6751-3693", "clpid": "Bryant-Zev" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Phillips", "given_name": "Rob", "orcid": "0000-0003-3082-2809", "clpid": "Phillips-R" } ], "abstract": "Active matter systems can generate highly ordered structures, avoiding equilibrium through the consumption of energy by individual constituents. How the microscopic parameters that characterize the active agents are translated to the observed mesoscopic properties of the assembly has remained an open question. These active systems are prevalent in living matter; for example, in cells, the cytoskeleton is organized into structures such as the mitotic spindle through the coordinated activity of many motor proteins walking along microtubules. Here, we investigate how the microscopic motor-microtubule interactions affect the coherent structures formed in a reconstituted motor-microtubule system. This question is of deeper evolutionary significance as we suspect motor and microtubule type contribute to the shape and size of resulting structures. We explore key parameters experimentally and theoretically, using a variety of motors with different speeds, processivities, and directionalities. We demonstrate that aster size depends on the motor used to create the aster, and develop a model for the distribution of motors and microtubules in steady-state asters that depends on parameters related to motor speed and processivity. Further, we show that network contraction rates scale linearly with the single-motor speed in quasi one-dimensional contraction experiments. In all, this theoretical and experimental work helps elucidate how microscopic motor properties are translated to the much larger scale of collective motor-microtubule assemblies.", "doi": "10.1101/2021.10.22.465381", "publication_date": "2021-10-23" }, { "id": "authors:vaf5k-ggg50", "collection": "authors", "collection_id": "vaf5k-ggg50", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220816-220022495", "type": "monograph", "title": "Cell density controls signal propagation waves in a multicellular synthetic gene circuit", "author": [ { "family_name": "Santorelli", "given_name": "Marco", "orcid": "0000-0002-9633-7825", "clpid": "Santorelli-Marco" }, { "family_name": "Bhamidipati", "given_name": "Pranav", "clpid": "Bhamidipati-Pranav" }, { "family_name": "Kavanagh", "given_name": "Andriu", "clpid": "Kavanagh-Andriu" }, { "family_name": "Fitts", "given_name": "Victoria", "clpid": "Fitts-Victoria" }, { "family_name": "Sondkar", "given_name": "Trusha", "clpid": "Sondkar-Trusha" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Morsut", "given_name": "Leonardo", "orcid": "0000-0001-7049-3478", "clpid": "Morsut-Leonardo" } ], "abstract": "During organismal development, biochemical reaction networks sense and respond to mechanical forces to coordinate embryonic patterning with embryo morphogenesis. Factors such as cortical tension, cell density, and matrix mechanical properties influence differentiation and cell fate decisions by modulating gene regulatory signaling networks. A major goal in synthetic development is to construct gene regulatory circuits that program the patterning and morphogenesis of synthetic multicellular structures. However, in the synthetic context, little is known regarding how the physical properties of the growth environment impact the behavior of synthetic gene circuits. Here, we exploit physical-chemical coupling observed in a synthetic patterning circuit in order to control the size and spatial distribution of patterned synthetic cell sheets. We show that cell density attenuates the propagation of signal between neighboring cells in a multicellular sheet containing a contact-dependent patterning circuit based on the synNotch signaling system. Density-dependent attenuation leads to a signal propagation wave that exhibits distinct qualitative phases of persistent propagation, transient propagation, and no propagation. Through computational modeling, we demonstrate that cell growth parameters determine the phase of propagation observed within a growing cell sheet. Using growth-modulating drugs and spatial density gradients, we control the size of synNotch-activated cell populations and generate tissue-scale activation gradients and kinematic waves. Our study reveals that density-dependent synNotch activity can be exploited to control a synthetic multicellular patterning circuit. More broadly, we show that synthetic gene circuits can be critically impacted by their physical context, providing an alternate means for programming circuit behavior.", "doi": "10.48550/arXiv.2107.08116", "publisher": "arXiv", "publication_date": "2021-07-16" }, { "id": "authors:qyx34-m3k70", "collection": "authors", "collection_id": "qyx34-m3k70", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20220816-220019094", "type": "monograph", "title": "Signaling receptor localization maximizes cellular information acquisition in spatially-structured, natural environments", "author": [ { "family_name": "Wang", "given_name": "Zitong Jerry", "orcid": "0000-0001-8008-7318", "clpid": "Wang-Zitong-Jerry" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Cells in natural environments like tissue or soil sense and respond to extracellular ligands with intricately structured and non-monotonic spatial distributions that are sculpted by processes such as fluid flow and substrate adhesion. Nevertheless, traditional approaches to studying cell sensing assume signals are either uniform or monotonic, neglecting spatial structures of natural environments. In this work, we show that spatial sensing and navigation can be optimized by adapting the spatial organization of signaling pathways to the spatial structure of the environment. By viewing cell surface receptors as a sensor network, we develop an information theoretic framework for computing the optimal spatial organization of a sensing system for a given spatial signaling environment. Applying the framework to simulated environments, we find that spatial receptor localization maximizes information acquisition in many natural contexts, including tissue and soil. Receptor localization extends naturally to produce a dynamic protocol for redistributing signaling receptors during cell navigation and can be implemented in a cell using a feedback scheme. In a simulated tissue environment, dynamic receptor localization boosts navigation efficiency by 30-fold. Broadly, our framework readily adapts to studying how the spatial organization of signaling components other than receptors can be modulated to improve cellular information processing.", "doi": "10.48550/arXiv.2107.00806", "publisher": "arXiv", "publication_date": "2021-07-02" }, { "id": "authors:ygmbj-pa530", "collection": "authors", "collection_id": "ygmbj-pa530", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210622-154854635", "type": "monograph", "title": "Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data", "author": [ { "family_name": "Chen", "given_name": "Xiaoqiao", "clpid": "Chen-Xiaoqiao" }, { "family_name": "Chen", "given_name": "Sisi", "orcid": "0000-0001-9448-9713", "clpid": "Chen-Sisi" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Sequencing costs currently prohibit the application of single-cell mRNA-seq to many biological and clinical analyses. Targeted single-cell mRNA-sequencing reduces sequencing costs by profiling reduced gene sets that capture biological information with a minimal number of genes. Here, we introduce an active learning method (ActiveSVM) that identifies minimal but highly-informative gene sets that enable the identification of cell-types, physiological states, and genetic perturbations in single-cell data using a small number of genes. Our active feature selection procedure generates minimal gene sets from single-cell data through an iterative cell-type classification task where misclassified cells are examined at each round of analysis to identify maximally informative genes through an `active' support vector machine (ActiveSVM) classifier. By focusing computational resources on misclassified cells, ActiveSVM scales to analyze data sets with over a million single cells. We demonstrate that ActiveSVM feature selection identifies gene sets that enable ~90% cell-type classification accuracy across a variety of data sets including cell atlas and disease characterization data sets. The method generalizes to reveal genes that respond to genetic perturbations and to identify region specific gene expression patterns in spatial transcriptomics data. The discovery of small but highly informative gene sets should enable substantial reductions in the number of measurements necessary for application of single-cell mRNA-seq to clinical tests, therapeutic discovery, and genetic screens.", "doi": "10.1101/2021.06.15.448478", "publication_date": "2021-06-16" }, { "id": "authors:3yzaq-fw923", "collection": "authors", "collection_id": "3yzaq-fw923", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20210217-132137942", "type": "monograph", "title": "Programming Boundary Deformation Patterns in Active Networks", "author": [ { "family_name": "Qu", "given_name": "Zijie", "clpid": "Qu-Zijie" }, { "family_name": "Jiang", "given_name": "Jialong", "clpid": "Jiang-Jialong" }, { "family_name": "Lee", "given_name": "Heun Jin", "clpid": "Lee-Heun-Jin" }, { "family_name": "Phillips", "given_name": "Rob", "orcid": "0000-0003-3082-2809", "clpid": "Phillips-R" }, { "family_name": "Shadkhoo", "given_name": "Shahriar", "clpid": "Shadkhoo-Shahriar" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Active materials take advantage of their internal sources of energy to self-organize in an automated manner. This feature provides a novel opportunity to design micron-scale machines with minimal required control. However, self-organization goes hand in hand with predetermined dynamics that are hardly susceptible to environmental perturbations. Therefore utilizing this feature of active systems requires harnessing and directing the macroscopic dynamics to achieve specific functions; which in turn necessitates understanding the underlying mechanisms of active forces. Here we devise an optical control protocol to engineer the dynamics of active networks composed of microtubules and light-activatable motor proteins. The protocol enables carving activated networks of different shapes, and isolating them from the embedding solution. Studying a large set of shapes, we observe that the active networks contract in a shape-preserving manner that persists over the course of contraction. We formulate a coarse-grained theory and demonstrate that self-similarity of contraction is associated with viscous-like active stresses. These findings help us program the dynamics of the network through manipulating the light intensity in space and time, and maneuver the network into bending in specific directions, as well as temporally alternating directions. Our work improves understanding the active dynamics in contractile networks, and paves a new path towards engineering the dynamics of a large class of active materials.", "doi": "10.48550/arXiv.2101.08464", "publisher": "arXiv", "publication_date": "2021-01-21" }, { "id": "authors:yvded-18923", "collection": "authors", "collection_id": "yvded-18923", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20200727-093309639", "type": "monograph", "title": "Enabling out-of-clinic human immunity studies via single-cell profiling of capillary blood", "author": [ { "family_name": "Dobreva", "given_name": "Tatyana", "orcid": "0000-0002-2625-8873", "clpid": "Dobreva-T" }, { "family_name": "Brown", "given_name": "David", "orcid": "0000-0002-9757-1744", "clpid": "Brown-David" }, { "family_name": "Park", "given_name": "Jong Hwee", "clpid": "Park-Jong-Hwee" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "An individual's immune system is driven by both genetic and environmental factors that vary over time. To better understand the temporal and inter-individual variability of gene expression within distinct immune cell types, we developed a platform that leverages multiplexed single-cell sequencing and out-of-clinic capillary blood extraction to enable simplified, cost-effective profiling of the human immune system across people and time at single-cell resolution. Using the platform, we detect widespread differences in cell type-specific gene expression between subjects that are stable over multiple days.", "doi": "10.1101/2020.07.25.210468", "publication_date": "2020-07-26" }, { "id": "authors:hmsx9-hxy52", "collection": "authors", "collection_id": "hmsx9-hxy52", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20200102-083334191", "type": "monograph", "title": "Designing signaling environments to steer transcriptional diversity in neural progenitor cell populations", "author": [ { "family_name": "Chen", "given_name": "Sisi", "orcid": "0000-0001-9448-9713", "clpid": "Chen-Sisi" }, { "family_name": "Park", "given_name": "Jong H.", "clpid": "Park-Jong-Hwee" }, { "family_name": "Jiang", "given_name": "Jialong", "clpid": "Jiang-Jialong" }, { "family_name": "Tsou", "given_name": "Tiffany", "orcid": "0000-0002-5651-2879", "clpid": "Tsou-Tiffany" }, { "family_name": "Rivaud", "given_name": "Paul", "orcid": "0000-0001-8637-3331", "clpid": "Rivaud-Paul" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "Stem and progenitor populations within developing embryos are diverse, composed of different subpopulations of precursor cells with varying developmental potential. How these different subpopulations are coordinately regulated by their signaling environments is not well understood. In this paper we develop a framework for controlling progenitor population structure in cell culture using high-throughput single cell mRNA-seq and computational analysis. We find that the natural transcriptional diversity of neural stem cell populations from the developing mouse brain collapses during in vitro culture. Cell populations are depleted of committed neuroblast progenitors and become dominated by a single pre-astrocytic cell population. By analyzing the response of neural stem cell populations to forty combinatorial signaling conditions, we demonstrate that signaling environments can restructure cell populations by modulating the relative abundance of pre-astrocytic and pre-neuronal subpopulations according to a simple log-linear model. Our work demonstrates that single-cell RNA-seq can be applied to learn how to modulate the diversity of stem cell populations, providing a new strategy for population-level stem cell control.", "publisher": "Caltech Library", "publication_date": "2019-12-31" }, { "id": "authors:cwa0t-yys04", "collection": "authors", "collection_id": "cwa0t-yys04", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20190520-085022351", "type": "monograph", "title": "Active Learning of Spin Network Models", "author": [ { "family_name": "Jiang", "given_name": "Jialong", "clpid": "Jiang-Jialong" }, { "family_name": "Sivak", "given_name": "David A.", "clpid": "Sivak-D-A" }, { "family_name": "Thomson", "given_name": "Matt", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" } ], "abstract": "The inverse statistical problem of finding direct interactions in complex networks is difficult. In the context of the experimental sciences, well-controlled perturbations can be applied to a system, probing the internal structure of the network. Therefore, we propose a general mathematical framework to study inference with iteratively applied perturbations to a network. Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information. Perturbations are then chosen that reduce most the variance of the Bayesian posterior. We apply this framework to a specific probabilistic graphical model where the nodes in the network are modeled as binary variables, \"spins\" with Ising-form pairwise interactions. Based on this strategy, we significantly improve the accuracy and efficiency of inference from a reasonable number of experimental queries for medium sized networks. Our active learning framework could be powerful in the analysis of complex networks as well as in the rational design of experiments.", "doi": "10.48550/arXiv.1903.10474", "publisher": "arXiv", "publication_date": "2019-03-25" }, { "id": "authors:ak61x-2zg81", "collection": "authors", "collection_id": "ak61x-2zg81", "cite_using_url": "https://resolver.caltech.edu/CaltechAUTHORS:20200121-074722672", "type": "monograph", "title": "Changes in epithelial proportions and transcriptional state underlie major premenopausal breast cancer risks", "author": [ { "family_name": "Murrow", "given_name": "Lyndsay M.", "orcid": "0000-0002-5935-8977", "clpid": "Murrow-Lyndsay-M" }, { "family_name": "Weber", "given_name": "Robert J.", "clpid": "Weber-Robert-J" }, { "family_name": "Caruso", "given_name": "Joseph A.", "clpid": "Caruso-Joseph-A" }, { "family_name": "McGinnis", "given_name": "Christopher S.", "clpid": "McGinnis-Christopher-S" }, { "family_name": "Phong", "given_name": "Kiet", "clpid": "Phong-Kiet" }, { "family_name": "Gascard", "given_name": "Philippe", "clpid": "Gascard-Philippe" }, { "family_name": "Borowsky", "given_name": "Alexander D.", "clpid": "Borowsky-Alexander-D" }, { "family_name": "Desai", "given_name": "Tejal A.", "clpid": "Desai-Tejal-A" }, { "family_name": "Thomson", "given_name": "Matthew", "orcid": "0000-0003-1021-1234", "clpid": "Thomson-M-W" }, { "family_name": "Tlsty", "given_name": "Thea", "clpid": "Tlsty-Thea" }, { "family_name": "Gartner", "given_name": "Zev J.", "clpid": "Gartner-Zev-J" } ], "abstract": "The human breast undergoes lifelong remodeling in response to estrogen and progesterone, but hormone exposure also increases breast cancer risk. Here, we use single-cell analysis to identify distinct mechanisms through which breast composition and cell state affect hormone signaling. We show that prior pregnancy reduces the transcriptional response of hormone-responsive (HR+) epithelial cells, whereas high body mass index (BMI) reduces overall HR+ cell proportions. These distinct changes both impact neighboring cells by effectively reducing the magnitude of paracrine signals originating from HR+ cells. Because pregnancy and high BMI are known to protect against hormone-dependent breast cancer in premenopausal women, our findings directly link breast cancer risk with person-to-person heterogeneity in hormone responsiveness. More broadly, our findings illustrate how cell proportions and cell state can collectively impact cell communities through the action of cell-to-cell signaling networks.", "doi": "10.1101/430611", "publication_date": "2018-09-29" } ]