Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues.

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

Impact of Memory T Cells on SARS-COV-2 Vaccine Response in Hematopoietic Stem Cell Transplant.

During the COVID-19 pandemic, hematopoietic stem cell transplant (HSCT) recipients faced an elevated mortality rate from SARS-CoV-2 infection, ranging between 10-40%. The SARS-CoV-2 mRNA vaccines are important tools in preventing severe disease, yet their efficacy in the post-transplant setting remains unclear, especially in patients subjected to myeloablative chemotherapy and immunosuppression. We evaluated the humoral and adaptive immune responses to the SARS-CoV-2 mRNA vaccination series in 42 HSCT recipients and 5 healthy controls. Peripheral blood mononuclear nuclear cells and serum were prospectively collected before and after each dose of the SARS-CoV-2 vaccine. Post-vaccination responses were assessed by measuring anti-spike IgG and nucleocapsid titers, and antigen specific T cell activity, before and after vaccination. In order to examine mechanisms behind a lack of response, pre-and post-vaccine samples were selected based on humoral and cellular responses for single-cell RNA sequencing with TCR and BCR sequencing. Our observations revealed that while all participants eventually mounted a humoral response, transplant recipients had defects in memory T cell populations that were associated with an absence of T cell response, some of which could be detected pre-vaccination.

The technological landscape and applications of single-cell multi-omics.

Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.

Microfluidic Immuno-Serolomic Assay Reveals Systems Level Association with COVID-19 Pathology and Vaccine Protection.

How to develop highly informative serology assays to evaluate the quality of immune protection against coronavirus disease-19 (COVID-19) has been a global pursuit over the past years. Here, a microfluidic high-plex immuno-serolomic assay is developed to simultaneously measure50 plasma or serum samples for50 soluble markers including 35proteins, 11 anti-spike/receptor binding domian (RBD) IgG antibodies spanningmajor variants, and controls. This assay demonstrates the quintuplicate test in a single run with high throughput, low sample volume, high reproducibilityand accuracy. It is applied to the measurement of 1012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein analysis reveals distinct immune mediator modules that exhibit a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies or receiving B cell depletion therapy. Serological analysis identifies that COVID-infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which can be associated with limited clonotype diversity and functional deficiency in B cells. These findings underscore the importance to individualize immunization strategies for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

Cross-platform analysis reveals cellular and molecular landscape of glioblastoma invasion.

BACKGROUND: Improved treatment of glioblastoma (GBM) needs to address tumor invasion, a hallmark of the disease that remains poorly understood. In this study, we profiled GBM invasion through integrative analysis of histological and single-cell RNA sequencing (scRNA-seq) data from 10 patients. METHODS: Human histology samples, patient-derived xenograft mouse histology samples, and scRNA-seq data were collected from 10 GBM patients. Tumor invasion was characterized and quantified at the phenotypic level using hematoxylin and eosin and Ki-67 histology stains. Crystallin alpha B (CRYAB) and CD44 were identified as regulators of tumor invasion from scRNA-seq transcriptomic data and validated in vitro, in vivo, and in a mouse GBM resection model. RESULTS: At the cellular level, we found that invasive GBM are less dense and proliferative than their non-invasive counterparts. At the molecular level, we identified unique transcriptomic features that significantly contribute to GBM invasion. Specifically, we found that CRYAB significantly contributes to postoperative recurrence and is highly co-expressed with CD44 in invasive GBM samples. CONCLUSIONS: Collectively, our analysis identifies differentially expressed features between invasive and nodular GBM, and describes a novel relationship between CRYAB and CD44 that contributes to tumor invasiveness, establishing a cellular and molecular landscape of GBM invasion.

Microfluidic immuno-serology assay revealed a limited diversity of protection against COVID-19 in patients with altered immunity.

The immune response to SARS-CoV-2 for patients with altered immunity such as hematologic malignancies and autoimmune disease may differ substantially from that in general population. These patients remain at high risk despite wide-spread adoption of vaccination. It is critical to examine the differences at the systems level between the general population and the patients with altered immunity in terms of immunologic and serological responses to COVID-19 infection and vaccination. Here, we developed a novel microfluidic chip for high-plex immuno-serological assay to simultaneously measure up to 50 plasma or serum samples for up to 50 soluble markers including 35 plasma proteins, 11 anti-spike/RBD IgG antibodies spanning all major variants, and controls. Our assay demonstrated the quintuplicate test in a single run with high throughput, low sample volume input, high reproducibility and high accuracy. It was applied to the measurement of 1,012 blood samples including in-depth analysis of sera from 127 patients and 21 healthy donors over multiple time points, either with acute COVID infection or vaccination. The protein association matrix analysis revealed distinct immune mediator protein modules that exhibited a reduced degree of diversity in protein-protein cooperation in patients with hematologic malignancies and patients with autoimmune disorders receiving B cell depletion therapy. Serological analysis identified that COVID infected patients with hematologic malignancies display impaired anti-RBD antibody response despite high level of anti-spike IgG, which could be associated with limited clonotype diversity and functional deficiency in B cells and was further confirmed by single-cell BCR and transcriptome sequencing. These findings underscore the importance to individualize immunization strategy for these high-risk patients and provide an informative tool to monitor their responses at the systems level.

Single-cell antigen-specific landscape of CAR T infusion product identifies determinants of CD19-positive relapse in patients with ALL.

A notable number of acute lymphoblastic leukemia (ALL) patients develop CD19-positive relapse within 1 year after receiving chimeric antigen receptor (CAR) T cell therapy. It remains unclear if the long-term response is associated with the characteristics of CAR T cells in infusion products, hindering the identification of biomarkers to predict therapeutic outcomes. Here, we present 101,326 single-cell transcriptomes and surface protein landscape from the infusion products of 12 ALL patients. We observed substantial heterogeneity in the antigen-specific activation states, among which a deficiency of T helper 2 function was associated with CD19-positive relapse compared with durable responders (remission, >54 months). Proteomic data revealed that the frequency of early memory T cells, rather than activation or coinhibitory signatures, could distinguish the relapse. These findings were corroborated by independent functional profiling of 49 patients, and an integrative model was developed to predict the response. Our data unveil the molecular mechanisms that may inform strategies to boost specific T cell function to maintain long-term remission.

Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level.

Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.

Spatial multi-omics sequencing for fixed tissue via DBiT-seq.

This protocol describes the use of the deterministic barcoding in tissue for spatial omics sequencing platform to construct a multi-omics atlas on fixed frozen tissue samples. This approach uses a microfluidic-based method to introduce combinatorial DNA oligo barcodes directly to the cells in a tissue section fixed on a glass slide. This technique does not directly resolve single cells but can achieve a near-single-cell resolution for spatial transcriptomics and spatial analysis of a targeted panel of proteins. For complete details on the use and execution of this protocol, please refer to Liu et al. (2020).

Single-cell multiomics dissection of basal and antigen-specific activation states of CD19-targeted CAR T cells.

BACKGROUND: Autologous T cells engineered to express a chimeric antigen receptor (CAR) specific for CD19 molecule have transformed the therapeutic landscape in patients with highly refractory leukemia and lymphoma, and the use of donor-generated allogeneic CAR T is paving the way for further breakthroughs in the treatment of cancer. However, it remains unknown how the intrinsic heterogeneities of these engineered cells mediate therapeutic efficacy and whether allogeneic products match the effectiveness of autologous therapies. METHODS: Using single-cell mRNA sequencing in conjunction with CITE-seq, we performed multiomics characterization of CAR T cells generated from healthy donor and patients with acute lymphoblastic leukemia. CAR T cells used in this study were manufactured at the University of Pennsylvania through lentiviral transduction with a CD19-4-1BB-CD3ζ construct. Besides the baseline condition, we engineered NIH-3T3 cells with human CD19 or mesothelin expression to conduct ex vivo antigen-specific or non-antigen stimulation of CAR T cells through 6-hour coculture at a 1:1 ratio. RESULTS: We delineated the global cellular and molecular CAR T landscape and identified that transcriptional CAR tonic signaling was regulated by a mixture of early activation, exhaustion signatures, and cytotoxic activities. On CD19 stimulation, we illuminated the disparities of CAR T cells derived from different origins and found that donor CAR T had more pronounced activation level in correlation with the upregulation of major histocompatibility complex class II genes compared with patient CAR T cells. This finding was independently validated in additional datasets from literature. Furthermore, GM-CSF(CSF2) expression was found to be associated with functional gene productions, but it induced little impact on the CAR T activation. CONCLUSIONS: Through integrated multiomics profiling and unbiased canonical pathway analyses, our results unveil heterogeneities in the transcriptional, phenotypic, functional, and metabolic profiles of donor and patient CAR T cells, providing mechanistic basis for ameliorating clinical outcomes and developing next-generation 'off- the-shelf' allogeneic products.

Single-cell Analysis Technologies for Immuno-oncology Research: from Mechanistic Delineation to Biomarker Discovery.

The successes with immune checkpoint blockade (ICB) and chimeric antigen receptor (CAR)-T-cell therapy in treating multiple cancer types have established immunotherapy as a powerful curative option for patients with advanced cancers. Unfortunately, many patients do not derive benefit or long-term responses, highlighting a pressing need to perform complete investigation of the underlying mechanisms and the immunotherapy-induced tumor regression or rejection. In recent years, a large number of single-cell technologies have leveraged advances in characterizing immune system, profiling tumor microenvironment, and identifying cellular heterogeneity, which establish the foundations for lifting the veil on the comprehensive crosstalk between cancer and immune system during immunotherapies. In this review, we introduce the applications of the most widely used single-cell technologies in furthering our understanding of immunotherapies in terms of underlying mechanisms and their association with therapeutic outcomes. We also discuss how single-cell analyses help to deliver new insights into biomarker discovery to predict patient response rate, monitor acquired resistance, and support prophylactic strategy development for toxicity management. Finally, we provide an overview of applying cutting-edge single-cell spatial-omics to point out the heterogeneity of tumor-immune interactions at higher level that can ultimately guide to the rational design of next-generation immunotherapies.

High-Spatial-Resolution Multi-Omics Sequencing via Deterministic Barcoding in Tissue.

We present deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq) for co-mapping of mRNAs and proteins in a formaldehyde-fixed tissue slide via next-generation sequencing (NGS). Parallel microfluidic channels were used to deliver DNA barcodes to the surface of a tissue slide, and crossflow of two sets of barcodes, A1-50 and B1-50, followed by ligation in situ, yielded a 2D mosaic of tissue pixels, each containing a unique full barcode AB. Application to mouse embryos revealed major tissue types in early organogenesis as well as fine features like microvasculature in a brain and pigmented epithelium in an eye field. Gene expression profiles in 10-µm pixels conformed into the clusters of single-cell transcriptomes, allowing for rapid identification of cell types and spatial distributions. DBiT-seq can be adopted by researchers with no experience in microfluidics and may find applications in a range of fields including developmental biology, cancer biology, neuroscience, and clinical pathology.

An Integrated Dielectrophoresis-Trapping and Nanowell Transfer Approach to Enable Double-Sub-Poisson Single-Cell RNA Sequencing.

Current technologies for high-throughput single-cell RNA sequencing (scRNA-seq) are based upon stochastic pairing of cells and barcoded beads in nanoliter droplets or wells. They are limited by the mathematical principle of the Poisson statistics such that the utilization of either cells or beads or both is no more than ∼33%. Despite the versatile design of microfluidics or microwells for high-yield loading of beads that beats the Poisson limit, subsequent encapsulation of single cells is still determined by stochastic pairing, representing a fundamental limitation in the field of single-cell sequencing. Here, we present dTNT-seq, an integrated dielectrophoresis (DEP)-trapping-nanowell-transfer (dTNT) approach to perform cell trapping and bead loading both in a sub-Poisson manner to facilitate scRNA-seq. A larger-sized 50 µm microwell array was prealigned precisely on top of the 20 µm DEP nanowell array such that single cells trapped by DEP can be readily transferred into the underneath larger wells by flipping the device, followed by subsequent hydrodynamic bead loading and coisolation with transferred single cells. Using a dTNT device composed of 3600 electroactive DEP-nanowell units, we demonstrated a single-cell trapping rate of 91.84%, a transfer efficiency of 82%, and a routine bead loading rate of >99%, which breaks the Poisson limit for the capture of both cells and beads, thus called double-sub-Poisson distribution, prior to encapsulating them in nanoliter wells for cellular mRNA barcoding. This approach was applied to human (HEK) and mouse (3T3) cells. Comparison with a non-DEP-based method through gene expression clustering and regulatory pathway analysis demonstrates consistent patterns and negligible alternation of cellular transcriptional states by DEP. We envision the dTNT-seq device can be modified for studying cell-cell interactions and enable other applications requiring active manipulation of single cells prior to transcriptome sequencing.

Ultrasonic Phased Array Compressive Imaging in Time and Frequency Domain: Simulation, Experimental Verification and Real Application.

Embracing the fact that one can recover certain signals and images from far fewer measurements than traditional methods use, compressive sensing (CS) provides solutions to huge amounts of data collection in phased array-based material characterization. This article describes how a CS framework can be utilized to effectively compress ultrasonic phased array images in time and frequency domains. By projecting the image onto its Discrete Cosine transform domain, a novel scheme was implemented to verify the potentiality of CS for data reduction, as well as to explore its reconstruction accuracy. The results from CIVA simulations indicate that both time and frequency domain CS can accurately reconstruct array images using samples less than the minimum requirements of the Nyquist theorem. For experimental verification of three types of artificial flaws, although a considerable data reduction can be achieved with defects clearly preserved, it is currently impossible to break Nyquist limitation in the time domain. Fortunately, qualified recovery in the frequency domain makes it happen, meaning a real breakthrough for phased array image reconstruction. As a case study, the proposed CS procedure is applied to the inspection of an engine cylinder cavity containing different pit defects and the results show that orthogonal matching pursuit (OMP)-based CS guarantees the performance for real application.

A Small Leak Detection Method Based on VMD Adaptive De-Noising and Ambiguity Correlation Classification Intended for Natural Gas Pipelines.

In this study, a small leak detection method based on variational mode decomposition (VMD) and ambiguity correlation classification (ACC) is proposed. The signals acquired from sensors were decomposed using the VMD, and numerous components were obtained. According to the probability density function (PDF), an adaptive de-noising algorithm based on VMD is proposed for noise component processing and de-noised components reconstruction. Furthermore, the ambiguity function image was employed for analysis of the reconstructed signals. Based on the correlation coefficient, ACC is proposed to detect the small leak of pipeline. The analysis of pipeline leakage signals, using 1 mm and 2 mm leaks, has shown that proposed detection method can detect a small leak accurately and effectively. Moreover, the experimental results have shown that the proposed method achieved better performances than support vector machine (SVM) and back propagation neural network (BP) methods.

[Study on the Effect of Sodium Chloride Salt on Near-Infrared Spectroscopy of Glucose Aqueous Solution].

The sodium chloride (NaCl) salt has been reported to be associated with glucose metabolism. However, the effect of it on non-invasive detection of blood glucose using near-infrared spectroscopy is still an open question. The aim of this study was to investigate this affection through transform background correction analysis two-dimensional (2D) correlation synchronous spectrum and the partial least-squares (PLS) regression. First, the transmittances of glucose aqueous solutions with different NaCl content are collected and the pure water and NaCl aqueous solution are measured as the background. Results show that, the dissolving of NaCl in water changes the amplitude and position of the absorption peak of water. There are two negative peaks in 1 400 and 1 500~1 700 nm corrected spectra of NaCl aqueous obviously and the amplitude of peaks associated with NaCl concentration. That's because NaCl affect the molecular binding and vibration of water. Then the glucose aqueous solutions without NaCl and with NaCl are corrected by the spectra of pure water and NaCl aqueous solution, respectively. So we get the conclusion that NaCl also affect the combination of glucose and water molecules. And the two-dimensional correlation spectroscopy analysis is performed under the perturbation of glucose concentration. The slice spectra of synchronous correlation spectra show that, the adding of NaCl weakens the spectral variation due to glucose concentration change in the wavelength of 1 400 and 1 520~1 700 nm. Finally, the partial least square (PLS) regression models were built to quantitatively conduct the influence of NaCl on glucose prediction accuracy. Comparison results showed that, NaCl molecule in aqueous solution will deteriorate the model accuracy, where root mean square error of prediction increases with the NaCl content; the mean difference of predicted glucose concentration between models based on glucose aqueous solutions with NaCl and without NaCl, is linear with NaCl concentration in samples.