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Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or noisy and irrelevant features (e.g. measurements with low signal-to-noise ratio) can mask the underlying trajectory of study and hinder inference. Here, we present DELVE (dynamic selection of locally covarying features), an unsupervised feature selection method for identifying a representative subset of dynamically-expressed molecular features that recapitulates cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effect of unwanted sources of variation confounding inference, and instead models cell states from dynamic feature modules that constitute core regulatory complexes. Using simulations, single-cell RNA sequencing data, and iterative immunofluorescence imaging data in the context of the cell cycle and cellular differentiation, we demonstrate that DELVE selects features that more accurately characterize cell populations and improve the recovery of cell type transitions. This feature selection framework provides an alternative approach for improving trajectory inference and uncovering co-variation amongst features along a biological trajectory. DELVE is implemented as an open-source python package and is publicly available at:https://github.com/jranek/delve.

With the growing number of single-cell datasets collected under more complex experimental conditions, there is an opportunity to leverage single-cell variability to reveal deeper insights into how cells respond to perturbations. Many existing approaches rely on discretizing the data into clusters for differential gene expression (DGE), effectively ironing out any information unveiled by the single-cell variability across cell-types. In addition, DGE often assumes a statistical distribution that, if erroneous, can lead to false positive differentially expressed genes. Here, we present Cellograph: a semi-supervised framework that uses graph neural networks to quantify the effects of perturbations at single-cell granularity. Cellograph not only measures how prototypical cells are of each condition but also learns a latent space that is amenable to interpretable data visualization and clustering. The learned gene weight matrix from training reveals pertinent genes driving the differences between conditions. We demonstrate the utility of our approach on publicly-available datasets including cancer drug therapy, stem cell reprogramming, and organoid differentiation. Cellograph outperforms existing methods for quantifying the effects of experimental perturbations and offers a novel framework to analyze single-cell data using deep learning.

Modern single-cell data analysis relies on statistical testing (e.g. differential expression testing) to identify genes or proteins that are up-or down-regulated in relation to cell-types or clinical outcomes. However, existing algorithms for such statistical testing are often limited by technical noise and cellular heterogeneity, which lead to false-positive results. To constrain the analysis to a compact and phenotype-related cell population, differential abundance (DA) testing methods were employed to identify subgroups of cells whose abundance changed significantly in response to disease progression, or experimental perturbation. Despite the effectiveness of DA testing algorithms of identifying critical cell-states, there are no systematic benchmarking or comparative studies to compare their usages in practice. Herein, we performed the first comprehensive benchmarking study to objectively evaluate and compare the benefits and potential downsides of current state-of-the-art DA testing methods. We benchmarked six DA testing methods on several practical tasks, using both synthetic and real single-cell datasets. The task evaluated include, recognizing true DA subpopulations, appropriate handing of batch effects, runtime efficiency, and hyperparameter usability and robustness. Based on various evaluation results, this paper gives dataset-specific suggestions for the usage of DA testing methods.

OBJECTIVE: To determine whether single-cell and plasma proteomic elements of the host’s immune response to surgery accurately identifies patients who develop a Surgical Site Complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients’ immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on post-operative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n=11) and without (n=30) an SSC (AUC = 0.86). Model features included co-regulated pro-inflammatory (e.g. IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (e.g. JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients’ immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

The longitudinal assessment of physical function with high temporal resolution at a scalable and objective level in patients recovering from surgery is highly desirable to understand the biological and clinical factors that drive the clinical outcome. However, physical recovery from surgery itself remains poorly defined and the utility of wearable technologies to study recovery after surgery has not been established.Prolonged postoperative recovery is often associated with long-lasting impairment of physical, mental, and social functions. While phenotypical and clinical patient characteristics account for some variation of individual recovery trajectories, biological differences likely play a major role. Specifically, patient-specific immune states have been linked to prolonged physical impairment after surgery. However, current methods of quantifying physical recovery lack patient specificity and objectivity.Here, a combined high-fidelity accelerometry and state-of-the-art deep immune profiling approach was studied in patients undergoing major joint replacement surgery. The aim was to determine whether objective physical parameters derived from accelerometry data can accurately track patient-specific physical recovery profiles (suggestive of a 'clock of postoperative recovery'), compare the performance of derived parameters with benchmark metrics including step count, and link individual recovery profiles with patients' preoperative immune state.The results of our models indicate that patient-specific temporal patterns of physical function can be derived with a precision superior to benchmark metrics. Notably, six distinct domains of physical function and sleep are identified to represent the objective temporal patterns: "activity capacity" and "moderate and overall activity" (declined immediately after surgery); "sleep disruption and sedentary activity" (increased after surgery); "overall sleep", "sleep onset", and "light activity" (no clear changes were observed after surgery). These patterns can be linked to individual patients' preoperative immune state using cross-validated canonical-correlation analysis. Importantly, the pSTAT3 signal activity in M-MDSCs predicted a slower recovery.Accelerometry-based recovery trajectories are scalable and objective outcomes to study patient-specific factors that drive physical recovery.

High-dimensional cytometry experiments measuring 20–50 cellular markers have become routine in many laboratories. The increased complexity of these datasets requires added rigor during the experimental planning and the subsequent manual and computational data analysis to avoid artefacts and misinterpretation of results. Here we discuss pitfalls frequently encountered during high-dimensional cytometry data analysis and aim to provide a basic framework and recommendations for reporting and analyzing these datasets.

Graph-based algorithms have become essential in the analysis of single-cell data for numerous tasks, such as automated cell-phenotyping and identifying cellular correlates of experimental perturbations or disease states. In large multi-patient, multi-sample single-cell datasets, the analysis of cell-cell similarity graphs representations of these data becomes computationally prohibitive. Here, we introduce cytocoarsening, a novel graph-coarsening algorithm that significantly reduces the size of single-cell graph representations, which can then used as input to downstream bioinformatics algorithms for improved computational efficiency. Uniquely, cytocoarsening considers both phenotypical similarity of cells and similarity of cells’ associated clinical or experimental attributes in order to more readily identify condition-specific cell populations. The resulting coarse graph representations were evaluated based on both their structural correctness and the capacity of downstream algorithms to uncover the same biological conclusions as if the full graph had been used. Cytocoarsening is provided as open source code at https://github.com/ChenCookie/cytocoarsening.

Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs’ effects on clinical outcomes likely dependent on functional adaptive immune responses., Erratum published online 03 September 2020 DOI: 10.1038/s41467-020-18410-y

Background Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the cell cycle, development, or disease progression. RNA velocity infers the direction and speed of transcriptional changes in individual cells, yet it is unclear how these temporal gene expression modalities may be leveraged for predictive modeling of cellular dynamics. Results Here, we present the first task-oriented benchmarking study that investigates integration of temporal sequencing modalities for dynamic cell state prediction. We benchmark ten integration approaches on ten datasets spanning different biological contexts, sequencing technologies, and species. We find that integrated data more accurately infers biological trajectories and achieves increased performance on classifying cells according to perturbation and disease states. Furthermore, we show that simple concatenation of spliced and unspliced molecules performs consistently well on classification tasks and can be used over more memory intensive and computationally expensive methods. Conclusions This work illustrates how integrated temporal gene expression modalities may be leveraged for predicting cellular trajectories and sample-associated perturbation and disease phenotypes. Additionally, this study provides users with practical recommendations for task-specific integration of single-cell gene expression modalities.

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell flow and mass cytometry technologies are being increasingly applied in clinical settings, as they enable the simultaneous measurement of multiple proteins across millions of cells within a multi-patient cohort. In this work, we introduce CytoSet, a deep learning model that can directly predict a patients clinical outcome from a collection of cells obtained through a blood or tissue sample. Unlike previous work, CytoSet explicitly models the cells profiled in each patient sample as a set, allowing for the use of recently developed permutation invariant architectures. We show that CytoSet achieves state-of-the-art classification performance across a variety of flow and mass cytometry benchmark datasets. Specifically, CytoSet greatly outperforms two baseline models by 20.6% on a large multi-sample clinical flow cytometry dataset. The strong classification performance is further complemented by demonstrated robustness to the number of sub-sampled cells per patient, enabling CytoSet to scale to hundreds of patient samples. Furthermore, we also conducted an ablation study with networks of varying depths to demonstrate that much of the representation power of CytoSet comes from the permutation-equivalent architectures. The superior performance achieved by the set-based architectures used in CytoSet suggests that clinical cytometry data can be appropriately interpreted and studied as sets. The code is publicly available at https://github.com/CompCy-lab/cytoset.

The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks.

Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M(+) (IgM(+)) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = −0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

All authors: Andrea Cossarizza Hyun‐Dong Chang Andreas Radbruch Andreas Acs Dieter Adam Sabine Adam‐Klages William W. Agace Nima Aghaeepour Mübeccel Akdis Matthieu Allez Larissa Nogueira Almeida Giorgia Alvisi Graham Anderson Immanuel Andrä Francesco Annunziato Achille Anselmo Petra Bacher Cosima T. Baldari Sudipto Bari Vincenzo Barnaba Joana Barros‐Martins Luca Battistini Wolfgang Bauer Sabine Baumgart Nicole Baumgarth Dirk Baumjohann Bianka Baying Mary Bebawy Burkhard Becher Wolfgang Beisker Vladimir Benes Rudi Beyaert Alfonso Blanco Dominic A. Boardman Christian Bogdan Jessica G. Borger Giovanna Borsellino Philip E. Boulais Jolene A. Bradford Dirk Brenner Ryan R. Brinkman Anna E. S. Brooks Dirk H. Busch Martin Büscher Timothy P. Bushnell Federica Calzetti Garth Cameron Ilenia Cammarata Xuetao Cao Susanna L. Cardell Stefano Casola Marco A. Cassatella Andrea Cavani Antonio Celada Lucienne Chatenoud Pratip K. Chattopadhyay Sue Chow Eleni Christakou Luka Čičin‐Šain Mario Clerici Federico S. Colombo Laura Cook Anne Cooke Andrea M. Cooper Alexandra J. Corbett Antonio Cosma Lorenzo Cosmi Pierre G. Coulie Ana Cumano Ljiljana Cvetkovic Van Duc Dang Chantip Dang‐Heine Martin S. Davey Derek Davies Sara De Biasi Genny Del Zotto Gelo Victoriano Dela Cruz Michael Delacher Silvia Della Bella Paolo Dellabona Günnur Deniz Mark Dessing James P. Di Santo Andreas Diefenbach Francesco Dieli Andreas Dolf Thomas Dörner Regine J. Dress Diana Dudziak Michael Dustin Charles‐Antoine Dutertre Friederike Ebner Sidonia B. G. Eckle Matthias Edinger Pascale Eede Götz R.A. Ehrhardt Marcus Eich Pablo Engel Britta Engelhardt Anna Erdei Charlotte Esser Bart Everts Maximilien Evrard Christine S. Falk Todd A. Fehniger Mar Felipo‐Benavent Helen Ferry Markus Feuerer Andrew Filby Kata Filkor Simon Fillatreau Marie Follo Irmgard Förster John Foster Gemma A. Foulds Britta Frehse Paul S. Frenette Stefan Frischbutter Wolfgang Fritzsche David W. Galbraith Anastasia Gangaev Natalio Garbi Brice Gaudilliere Ricardo T. Gazzinelli Jens Geginat Wilhelm Gerner Nicholas A. Gherardin Kamran Ghoreschi Lara Gibellini Florent Ginhoux Keisuke Goda Dale I. Godfrey Christoph Goettlinger Jose M. González‐Navajas Carl S. Goodyear Andrea Gori Jane L. Grogan Daryl Grummitt Andreas Grützkau Claudia Haftmann Jonas Hahn Hamida Hammad Günter Hämmerling Leo Hansmann Goran Hansson Christopher M. Harpur Susanne Hartmann Andrea Hauser Anja E. Hauser David L. Haviland David Hedley Daniela C. Hernández Guadalupe Herrera Martin Herrmann Christoph Hess Thomas Höfer Petra Hoffmann Kristin Hogquist Tristan Holland Thomas Höllt Rikard Holmdahl Pleun Hombrink Jessica P. Houston Bimba F. Hoyer Bo Huang Fang‐Ping Huang Johanna E. Huber Jochen Huehn Michael Hundemer Christopher A. Hunter William Y. K. Hwang Anna Iannone Florian Ingelfinger Sabine M Ivison Hans‐Martin Jäck Peter K. Jani Beatriz Jávega Stipan Jonjic Toralf Kaiser Tomas Kalina Thomas Kamradt Stefan H. E. Kaufmann Baerbel Keller Steven L. C. Ketelaars Ahad Khalilnezhad Srijit Khan Jan Kisielow Paul Klenerman Jasmin Knopf Hui‐Fern Koay Katja Kobow Jay K. Kolls Wan Ting Kong Manfred Kopf Thomas Korn Katharina Kriegsmann Hendy Kristyanto Thomas Kroneis Andreas Krueger Jenny Kühne Christian Kukat Désirée Kunkel Heike Kunze‐Schumacher Tomohiro Kurosaki Christian Kurts Pia Kvistborg Immanuel Kwok Jonathan Landry Olivier Lantz Paola Lanuti Francesca LaRosa Agnès Lehuen Salomé LeibundGut‐Landmann Michael D. Leipold Leslie Y.T. Leung Megan K. Levings Andreia C. Lino Francesco Liotta Virginia Litwin Yanling Liu Hans‐Gustaf Ljunggren Michael Lohoff Giovanna Lombardi Lilly Lopez Miguel López‐Botet Amy E. Lovett‐Racke Erik Lubberts Herve Luche Burkhard Ludewig Enrico Lugli Sebastian Lunemann Holden T. Maecker Laura Maggi Orla Maguire Florian Mair Kerstin H. Mair Alberto Mantovani Rudolf A. Manz Aaron J. Marshall Alicia Martínez‐Romero Glòria Martrus Ivana Marventano Wlodzimierz Maslinski Giuseppe Matarese Anna Vittoria Mattioli Christian Maueröder Alessio Mazzoni James McCluskey Mairi McGrath Helen M. McGuire Iain B. McInnes Henrik E. Mei Fritz Melchers Susanne Melzer Dirk Mielenz Stephen D. Miller Kingston H.G. Mills Hans Minderman Jenny Mjösberg Jonni Moore Barry Moran Lorenzo Moretta Tim R. Mosmann Susann Müller Gabriele Multhoff Luis Enrique Muñoz Christian Münz Toshinori Nakayama Milena Nasi Katrin Neumann Lai Guan Ng Antonia Niedobitek Sussan Nourshargh Gabriel Núñez José‐Enrique O'Connor Aaron Ochel Anna Oja Diana Ordonez Alberto Orfao Eva Orlowski‐Oliver Wenjun Ouyang Annette Oxenius Raghavendra Palankar Isabel Panse Kovit Pattanapanyasat Malte Paulsen Dinko Pavlinic Livius Penter Pärt Peterson Christian Peth Jordi Petriz Federica Piancone Winfried F. Pickl Silvia Piconese Marcello Pinti A. Graham Pockley Malgorzata Justyna Podolska Zhiyong Poon Katharina Pracht Immo Prinz Carlo E. M. Pucillo Sally A. Quataert Linda Quatrini Kylie M. Quinn Helena Radbruch Tim R. D. J. Radstake Susann Rahmig Hans‐Peter Rahn Bartek Rajwa Gevitha Ravichandran Yotam Raz Jonathan A. Rebhahn Diether Recktenwald Dorothea Reimer Caetano Reis e Sousa Ester B.M. Remmerswaal Lisa Richter Laura G. Rico Andy Riddell Aja M. Rieger J. Paul Robinson Chiara Romagnani Anna Rubartelli Jürgen Ruland Armin Saalmüller Yvan Saeys Takashi Saito Shimon Sakaguchi Francisco Sala‐de‐Oyanguren Yvonne Samstag Sharon Sanderson Inga Sandrock Angela Santoni Ramon Bellmàs Sanz Marina Saresella Catherine Sautes‐Fridman Birgit Sawitzki Linda Schadt Alexander Scheffold Hans U. Scherer Matthias Schiemann Frank A. Schildberg Esther Schimisky Andreas Schlitzer Josephine Schlosser Stephan Schmid Steffen Schmitt Kilian Schober Daniel Schraivogel Wolfgang Schuh Thomas Schüler Reiner Schulte Axel Ronald Schulz Sebastian R. Schulz Cristiano Scottá Daniel Scott‐Algara David P. Sester T. Vincent Shankey Bruno Silva‐Santos Anna Katharina Simon Katarzyna M. Sitnik Silvano Sozzani Daniel E. Speiser Josef Spidlen Anders Stahlberg Alan M. Stall Natalie Stanley Regina Stark Christina Stehle Tobit Steinmetz Hannes Stockinger Yousuke Takahama Kiyoshi Takeda Leonard Tan Attila Tárnok Gisa Tiegs Gergely Toldi Julia Tornack Elisabetta Traggiai Mohamed Trebak Timothy I.M. Tree Joe Trotter John Trowsdale Maria Tsoumakidou Henning Ulrich Sophia Urbanczyk Willem van de Veen Maries van den Broek Edwin van der Pol Sofie Van Gassen Gert Van Isterdael René A.W. van Lier Marc Veldhoen Salvador Vento‐Asturias Paulo Vieira David Voehringer Hans‐Dieter Volk Anouk von Borstel Konrad von Volkmann Ari Waisman Rachael V. Walker Paul K. Wallace Sa A. Wang Xin M. Wang Michael D. Ward Kirsten A Ward‐Hartstonge Klaus Warnatz Gary Warnes Sarah Warth Claudia Waskow James V. Watson Carsten Watzl Leonie Wegener Thomas Weisenburger Annika Wiedemann Jürgen Wienands Anneke Wilharm Robert John Wilkinson Gerald Willimsky James B. Wing Rieke Winkelmann Thomas H. Winkler Oliver F. Wirz Alicia Wong Peter Wurst Jennie H. M. Yang Juhao Yang Maria Yazdanbakhsh Liping Yu Alice Yue Hanlin Zhang Yi Zhao Susanne Maria Ziegler Christina Zielinski Jakob Zimmermann Arturo Zychlinsky., These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer‐reviewed by leading experts in the field, making this an essential research companion., This work was supported by the Netherlands Organisation for Scientific Research – Domain Applied and Engineering Sciences (NWO-TTW), research program VENI 15924. This work was funded by the Deutsche Forschungsgemeinschaft. European Union Innovative Medicines Initiative - Joint Undertaking - RTCure Grant Agreement 777357 and innovation program (Grant Agreement 695551).

Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear and that poses a threat to both mothers and infants. Specific complex changes in women's physiology precede a diagnosis of preeclampsia. Understanding multiple aspects of such a complex changes at different levels of biology, can be enabled by simultaneous application of multiple assays. We developed prediction models for preeclampsia risk by analyzing six omics datasets from a longitudinal cohort of pregnant women. A machine learning-based multiomics model had high accuracy (area under the receiver operating characteristics curve (AUC) of 0.94, 95% confidence intervals (CI): [0.90, 0.99]). A prediction model using only ten urine metabolites provided an accuracy of the whole metabolomic dataset and was validated using an independent cohort of 16 women (AUC = 0.87, 95% CI: [0.76, 0.99]). Integration with clinical variables further improved prediction accuracy of the urine metabolome model (AUC = 0.90, 95% CI: [0.80, 0.99], urine metabolome, validated). We identified several biological pathways to be associated with preeclampsia. The findings derived from models were integrated with immune system cytometry data, confirming known physiological alterations associated with preeclampsia and suggesting novel associations between the immune and proteomic dynamics. While further validation in larger populations is necessary, these encouraging results will serve as a basis for a simple, early diagnostic test for preeclampsia.

Community structure describes the organization of a network into subgraphs that contain a prevalence of edges within each subgraph and relatively few edges across boundaries between subgraphs. The development of community-detection methods has occurred across disciplines, with numerous and varied algorithms proposed to find communities. As we present in this Chapter via several case studies, community detection is not just an "end game" unto itself, but rather a step in the analysis of network data which is then useful for furthering research in the disciplinary domain of interest. These case-study examples arise from diverse applications, ranging from social and political science to neuroscience and genetics, and we have chosen them to demonstrate key aspects of community detection and to highlight that community detection, in practice, should be directed by the application at hand.

This diagnostic/prognostic study describes the use of cell-free transcriptomics, urine metabolomics, and plasma proteomics for identifying the biological measurements associated with preterm birth., Key Points Question What maternal biological modalities are associated with preterm birth (PTB)? Findings In this diagnostic/prognostic study of 81 pregnant women from 5 birth cohorts in low- and middle-income countries, several correlates of preterm birth in urine and blood were found to be associated with PTB. Although cohort-specific signatures were present, a machine learning algorithm was able to generate a model that was capable of predicting PTB across the cohorts. Meaning Results of this study suggest that most PTBs can be predicted using blood and urine samples collected early in the pregnancy, providing opportunities for interventions., Importance Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. Objective To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. Design, Setting, and Participants This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. Exposures Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. Main Outcomes and Measures The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. Results Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. Conclusions and Relevance This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Peripheral immune responses of Alzheimer’s disease patients are uniquely different from those of healthy and disease controls., Peripheral blood mononuclear cells (PBMCs) may provide insight into the pathogenesis of Alzheimer’s disease (AD) or Parkinson’s disease (PD). We investigated PBMC samples from 132 well-characterized research participants using seven canonical immune stimulants, mass cytometric identification of 35 PBMC subsets, and single-cell quantification of 15 intracellular signaling markers, followed by machine learning model development to increase predictive power. From these, three main intracellular signaling pathways were identified specifically in PBMC subsets from people with AD versus controls: reduced activation of PLCγ2 across many cell types and stimulations and selectively variable activation of STAT1 and STAT5, depending on stimulant and cell type. Our findings functionally buttress the now multiply-validated observation that a rare coding variant in PLCG2 is associated with a decreased risk of AD. Together, these data suggest enhanced PLCγ2 activity as a potential new therapeutic target for AD with a readily accessible pharmacodynamic biomarker.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.