Best practices for single-cell analysis across modalities.

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Population-level integration of single-cell datasets enables multi-scale analysis across samples.

The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

Predictors of Readmission and Reoperation Following Shoulder Arthroplasty in Patients Under 45 Years of Age.

BACKGROUND: The use of shoulder arthroplasty has increased among all age groups, albeit most prominently in older patients. While previous studies have investigated predictors of short-term readmission and reoperation in the general population, there is a paucity of literature available on these in patients under 45 years of age. This study aimed to identify the predictors of 30-day readmission and reoperation following shoulder arthroplasty in patients under 45 years of age. METHODS: A retrospective query in the American College of Surgeons National Surgical Quality Improvement Program database from 2011 to 2019 was used to identify patients who underwent primary reverse and anatomic total shoulder arthroplasty and hemiarthroplasty. Multivariate logistic regression was used to identify predictors of 30-day readmission and reoperation. RESULTS: A total of 530 patients were included. Multivariate regression revealed that Black race and Hispanic ethnicity were independent predictors of readmission. Functional dependence, hypertension requiring medication, and prolonged length of stay predicted reoperation. Finally, low hematocrit and prolonged length of stay predicted morbidity. DISCUSSION: Identifying and accounting for these risk factors for poor outcomes may help improve perioperative risk stratification. As a result, these findings have the potential to reduce healthcare costs associated with readmission and reoperation following shoulder arthroplasty in young patients. Our results also highlight the underlying disparities in healthcare outcomes among racial and ethnic groups that must be considered.

Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk.

Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

Kidney micro-organoids in suspension culture as a scalable source of human pluripotent stem cell-derived kidney cells.

Kidney organoids have potential uses in disease modelling, drug screening and regenerative medicine. However, novel cost-effective techniques are needed to enable scaled-up production of kidney cell types in vitro We describe here a modified suspension culture method for the generation of kidney micro-organoids from human pluripotent stem cells. Optimisation of differentiation conditions allowed the formation of micro-organoids, each containing six to ten nephrons that were surrounded by endothelial and stromal populations. Single cell transcriptional profiling confirmed the presence and transcriptional equivalence of all anticipated renal cell types consistent with a previous organoid culture method. This suspension culture micro-organoid methodology resulted in a three- to fourfold increase in final cell yield compared with static culture, thereby representing an economical approach to the production of kidney cells for various biological applications.

Evaluation of variability in human kidney organoids.

The utility of human pluripotent stem cell-derived kidney organoids relies implicitly on the robustness and transferability of the protocol. Here we analyze the sources of transcriptional variation in a specific kidney organoid protocol. Although individual organoids within a differentiation batch showed strong transcriptional correlation, we noted significant variation between experimental batches, particularly in genes associated with temporal maturation. Single-cell profiling revealed shifts in nephron patterning and proportions of component cells. Distinct induced pluripotent stem cell clones showed congruent transcriptional programs, with interexperimental and interclonal variation also strongly associated with nephron patterning. Epithelial cells isolated from organoids aligned with total organoids at the same day of differentiation, again implicating relative maturation as a confounder. This understanding of experimental variation facilitated an optimized analysis of organoid-based disease modeling, thereby increasing the utility of kidney organoids for personalized medicine and functional genomics.

Unique Transcriptional Architecture in Airway Epithelial Cells and Macrophages Shapes Distinct Responses following Influenza Virus Infection Ex Vivo.

Airway epithelial cells and macrophages differ markedly in their responses to influenza A virus (IAV) infection. To investigate transcriptional responses underlying these differences, purified subsets of type II airway epithelial cells (ATII) and alveolar macrophages (AM) recovered from the lungs of mock- or IAV-infected mice at 9 h postinfection were subjected to RNA sequencing. This time point was chosen to allow for characterization of cell types first infected with the virus inoculum, prior to multicycle virus replication and the infiltration of inflammatory cells into the airways. In the absence of infection, AM predominantly expressed genes related to immunity, whereas ATII expressed genes consistent with their physiological roles in the lung. Following IAV infection, AM almost exclusively activated cell-intrinsic antiviral pathways that were dependent on interferon (IFN) regulatory factor 3/7 (IRF3/7) and/or type I IFN signaling. In contrast, IAV-infected ATII activated a broader range of physiological responses, including cell-intrinsic antiviral pathways, which were both independent of and dependent on IRF3/7 and/or type I IFN. These data suggest that transcriptional profiles hardwired during development are a major determinant underlying the different responses of ATII and AM to IAV infection.IMPORTANCE Airway epithelial cells (AEC) and airway macrophages (AM) represent major targets of influenza A virus (IAV) infection in the lung, yet the two cell types respond very differently to IAV infection. We have used RNA sequencing to define the host transcriptional responses in each cell type under steady-state conditions as well as following IAV infection. To do this, different cell subsets isolated from the lungs of mock- and IAV-infected mice were subjected to RNA sequencing. Under steady-state conditions, AM and AEC express distinct transcriptional activities, consistent with distinct physiological roles in the airways. Not surprisingly, these cells also exhibited major differences in transcriptional responses following IAV infection. These studies shed light on how the different transcriptional architectures of airway cells from two different lineages drive transcriptional responses to IAV infection.

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database.

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database (www.scRNA-tools.org) to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time.

Circulating tumour cells from patients with colorectal cancer have cancer stem cell hallmarks in ex vivo culture.

OBJECTIVE: Although counting of circulating tumour cells (CTC) has attracted a broad interest as potential markers of tumour progression and treatment response, the lack of functional characterisation of these cells had become a bottleneck in taking these observations to the clinic. Our objective was to culture these cells in order to understand them and exploit their therapeutic potential to the full. DESIGN: Here, hypothesising that some CTC potentially have cancer stem cell (CSC) phenotype, we generated several CTC lines from the blood of patients with advanced metastatic colorectal cancer (CRC) based on their self-renewal abilities. Multiple standard tests were then employed to characterise these cells. RESULTS: Our CTC lines self-renew, express CSC markers and have multilineage differentiation ability, both in vitro and in vivo. Patient-derived CTC lines are tumorigenic in subcutaneous xenografts and are also able to colonise the liver after intrasplenic injection. RNA sequencing analyses strikingly demonstrate that drug metabolising pathways represent the most upregulated feature among CTC lines in comparison with primary CRC cells grown under similar conditions. This result is corroborated by the high resistance of the CTC lines to conventional cytotoxic compounds. CONCLUSIONS: Taken together, our results directly demonstrate the existence of patient-derived colorectal CTCs that bear all the functional attributes of CSCs. The CTC culture model described here is simple and takes <1 month from blood collection to drug testing, therefore, routine clinical application could facilitate access to personalised medicine. CLINICAL TRIAL REGISTRATION: ClinicalTrial.gov NCT01577511.

Ulnar-Sided Wrist Pain: A Diagnostic Evaluation Guide From 30-Plus Years of Experience.

INTRODUCTION: While multiple ulnar-sided wrist pain (USWP) diagnostic evaluation guides have been presented, none have included original clinical data or statistical analysis. The purpose of this study is to provide a diagnostic evaluation guide derived from original clinical data and analysis to help clinicians arrive at a differential diagnosis for USWP. METHODS: Using a computer search of patients presenting with sprains, instability, and laxity of the wrist, 385 patient charts were identified. Patient demographics, mechanism of injury, subjective complaints, physical findings, and diagnostic test findings were reviewed. Statistical analysis was performed to determine sensitivity and specificity of diagnostic methods on their ability to identify lunotriquetral ligament tears, triangular fibrocartilage complex (TFCC) tears, and ulnar impaction syndrome. Diagnostic arthroscopy was used as the reference standard. RESULTS: Ninety-three patients, comprising 101 cases of USWP, were included in the study. The onset of injury was traumatic in 83 out of 101 cases with motor vehicle accidents (N=46) being the most common, followed by overuse (N=18), and a fall onto an outstretched hand (N=16). The ulnocarpal tenderness test exhibited sensitivity/specificity of 72%/33%; lunotriquetral ligament laxity test of 42%/62%; bone scan of 80%/33%; radiocarpal arthrogram of 90%/98% for TFCC tears and 50%/91% for lunotriquetral ligament tears; midcarpal arthrogram of 82%/86% for lunotriquetral ligament tears. The mean ulnar variance on standard posteroanterior view radiograph was 0.95 mm, increasing to 2.67 mm on gripping posteroanterior view. CONCLUSION: Physicians should suspect a lunotriquetral ligament and/or TFCC tear with the acute onset of USWP following a loaded dorsiflexed mechanism of injury. Ulnocarpal tenderness tests and pre-operative ulnar variance measures are effective for increasing suspicion of USW pathology. Bone scans are helpful in diagnosing ulnar impaction syndrome in conjunction with radiographic findings. A combination of midcarpal arthrogram for lunotriquetral ligament tears and radiocarpal arthrogram for TFCC tears should be employed.

Integrating single-cell RNA-seq datasets with substantial batch effects.

Integration of single-cell RNA-sequencing (scRNA-seq) datasets has become a standard part of the analysis, with conditional variational autoencoders (cVAE) being among the most popular approaches. Increasingly, researchers are asking to map cells across challenging cases such as cross-organs, species, or organoids and primary tissue, as well as different scRNA-seq protocols, including single-cell and single-nuclei. Current computational methods struggle to harmonize datasets with such substantial differences, driven by technical or biological variation. Here, we propose to address these challenges for the popular cVAE-based approaches by introducing and comparing a series of regularization constraints. The two commonly used strategies for increasing batch correction in cVAEs, that is Kullback-Leibler divergence (KL) regularization strength tuning and adversarial learning, suffer from substantial loss of biological information. Therefore, we adapt, implement, and assess alternative regularization strategies for cVAEs and investigate how they improve batch effect removal or better preserve biological variation, enabling us to propose an optimal cVAE-based integration strategy for complex systems. We show that using a VampPrior instead of the commonly used Gaussian prior not only improves the preservation of biological variation but also unexpectedly batch correction. Moreover, we show that our implementation of cycle-consistency loss leads to significantly better biological preservation than adversarial learning implemented in the previously proposed GLUE model. Additionally, we do not recommend relying only on the KL regularization strength tuning for increasing batch correction, as it removes both biological and batch information without discriminating between the two. Based on our findings, we propose a new model that combines VampPrior and cycle-consistency loss. We show that using it for datasets with substantial batch effects improves downstream interpretation of cell states and biological conditions. To ease the use of the newly proposed model, we make it available in the scvi-tools package as an external model named sysVI. Moreover, in the future, these regularization techniques could be added to other established cVAE-based models to improve the integration of datasets with substantial batch effects.

Defining and benchmarking open problems in single-cell analysis.

With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.

Complications Following Endoscopic and Open Trigger Finger Release: A Retrospective Comparative Study.

BACKGROUND: Open trigger finger release (OTFR) and endoscopic trigger finger release (ETFR) are effective methods in treating stenosing tenosynovitis. However, a paucity of literature exists comparing the techniques. This study describes and compares postoperative complications following OTFR and ETFR at a single institution. METHODS: Patients undergoing trigger finger release between 2018 and 2020 within a single institution were identified. Electronic medical records were reviewed for patient demographics, surgical history, surgical characteristics, and clinical outcomes. Major and minor postoperative complications were assessed. Secondary outcome measures included tourniquet time and procedure time. Statistical analysis evaluated associations between postoperative complications, surgical technique, patient demographics, and surgical characteristics. RESULTS: In total, 57 patients (80 digits) were included in the study: 42 digits treated with OTFR and 38 digits treated with ETFR. Mean follow-up time was 57.6 ± 69.0 days (range, 7-307 days) for ETFR and 34.2 ± 26.3 days (range, 6-120 days) for OTFR. Overall, major, and minor complication rates for the cohort were 8.8%, 1.8% and 7.0%, respectively. There were no major complications following ETFR and 1 following OTFR (4%), the isolated case being postoperative Chronic regional pain syndrome. Minor complication rates were similar following OTFR (8%) and ETFR (6%). Persistent digit stiffness and swelling were found to be the most prevalent minor complications (n = 2, respectively), followed by wound dehiscence (n = 1). Female patients were significantly more likely to experience postoperative complications. CONCLUSIONS: Major complications following trigger finger release are unlikely; however, minor complications are prominent. Patients treated with OTFR and ETFR showed similar postoperative complication rates. Continued investigations into the benefits of ETFR are warranted.

Single-Portal Antegrade Endoscopic Trigger Finger Release: Cadaveric and Clinical Outcomes.

BACKGROUND: This study aimed to examine the relationship between anatomical surface landmarks in fresh frozen cadavers as related to in vivo endoscopic trigger finger release (ETFR) and present clinical outcomes after a single-portal antegrade ETFR technique. METHODS: Endoscopic trigger finger release was performed on 40 cadaveric digits. Each digit was dissected and the following measurements were recorded: distance from palmar digital crease and A1 pulley, length of the A1 pulley, percentage of A1 pulley released, and injury to vulnerable anatomy. A retrospective chart review was performed on 48 patients (62 digits) treated with ETFR. Outcome measures included grip and pinch strength, range of motion, Disability of Arm, Shoulder, and Hand (DASH) questionnaires, and Visual Analog Scale (VAS) pain scores. RESULTS: Release of the A1 pulley was achieved in 33 of the 40 cadaveric digits (83%) with an A2 pulley laceration rate of 25%. No flexor tendon or neurovascular injuries occurred. Gross grasp, lateral pinch, 3-jaw chuck, and precision pinch strength had 85%, 90%, 82%, and 90% recovery, respectively. At the final follow-up, average metacarpophalangeal joint, proximal interphalangeal joint, and distal interphalangeal joint range of motion were within the normal limits. Mean VAS scores decreased from 5.7 preoperatively to 1.0 postoperatively and mean DASH score at the final follow-up was 4.8. CONCLUSIONS: With the use of anatomical surface landmarks, ETFR may be performed in an efficient and reproducible manner. Patients treated with ETFR had low complication rates, good functional recovery, and improved pain at short-term follow-up. Further study of long-term outcomes and cost-effectiveness of ETFR is warranted.

Delineating mouse ß-cell identity during lifetime and in diabetes with a single cell atlas.

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin ß-cell ablation model. The ß-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that ß-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD ß-cells. We also report pathways that are shared between ß-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of ß-cell responses to different stressors, providing a roadmap for the understanding of ß-cell plasticity, compensation and demise.

Venous Thromboembolism Rates Have Not Decreased in Elective Lumbar Fusion Surgery from 2011 to 2020.

STUDY DESIGN: Retrospective cohort study. OBJECTIVES: This study aimed to (1) evaluate for any temporal trends in the rates of VTE, deep venous thrombosis (DVT), pulmonary embolism (PE), and mortality from 2011 to 2020 and (2) identify the predictors of VTE following lumbar fusion surgery. METHODS: Annual incidences of 30-day VTE, DVT, PE, and mortality were calculated for each of the operation year groups from 2011 to 2020. Multivariable Poisson regression was utilized to test the association between operation year and primary outcomes, as well as to identify significant predictors of VTE. RESULTS: A total of 121,205 patients were included. There were no statistically significant differences in VTE, DVT, PE, or mortality rates among the operation year groups. Multivariable regression analysis revealed that compared to 2011, operation year 2019 was associated with significantly lower rates of DVT. Age, BMI, prolonged operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking status, functional dependence, and chronic steroid use were identified as independent predictors of VTE following lumbar fusion. Female sex, Hispanic ethnicity, and outpatient surgery setting were identified as protective factors from VTE in this cohort. CONCLUSIONS: Rates of VTE after lumbar fusion have remained mostly unchanged between 2011 and 2020. Older age, higher BMI, longer operation time, prolonged length of stay, non-home discharge, anterior fusion, smoking, functional dependence, and steroid use were independent predictors of VTE after lumbar fusion, while female sex, Hispanic ethnicity, and outpatient surgery were the protective factors.

Clinical Outcomes of Lunocapitate Arthrodesis: A Retrograde Single-Screw Approach.

BACKGROUND: A single-site retrospective study was designed to evaluate the clinical outcomes of single-screw lunocapitate arthrodesis (LCA) using a retrograde approach for the treatment of scapholunate advanced collapse (SLAC) wrist. METHODS: We retrospectively identified 31 patients (33 cases) between September 2010 and December 2019 with SLAC wrist changes who were treated with single-screw LCA. Objective outcomes included time to fusion, union rate, range of motion, and grip and pinch strength recovery. Subjective outcomes included Disabilities of the Arm, Shoulder, and Hand (DASH) scores. RESULTS: We report on 33 cases (7 female), mean age 58.4 years (range: 41-85), with SLAC wrist who underwent LCA. Our cohort reported a 94% union rate and a 90-day mean time to fusion. Final active wrist range of motion was 38° dorsiflexion, 35° volarflexion, 17° radial deviation, 17° ulnar deviation, 82° pronation, and 83° supination (mean: 450.8 days). Final grip and pinch strengths recovered was 75% gross grip, 84% lateral pinch, and 75% precision pinch (mean: 379.0 days) compared with the contralateral side. The mean postoperative DASH score was 27 (mean: 1203.9 days). Two nonunions were observed. Two hardware complications occurred: one symptomatic screw and one screw fatigue fracture. CONCLUSIONS: We found retrograde single-screw LCA to be an effective salvage procedure for SLAC wrist. LCA is a less-taxing procedure, requires shorter operating time, and produces range of motion and grip and pinch strength recovery comparable to those of 4-corner arthrodesis. Furthermore, the viability of single-screw fixation may reduce hardware-related operative costs without compromising union rates.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape.

Recent years have seen a revolution in single-cell RNA-sequencing (scRNA-seq) technologies, datasets, and analysis methods. Since 2016, the scRNA-tools database has cataloged software tools for analyzing scRNA-seq data. With the number of tools in the database passing 1000, we provide an update on the state of the project and the field. This data shows the evolution of the field and a change of focus from ordering cells on continuous trajectories to integrating multiple samples and making use of reference datasets. We also find that open science practices reward developers with increased recognition and help accelerate the field.