scholarly journals SM-Omics: An automated platform for high-throughput spatial multi-omics

2020 ◽  
Author(s):  
Sanja Vickovic ◽  
Britta Lötstedt ◽  
Johanna Klughammer ◽  
Åsa Segerstolpe ◽  
Orit Rozenblatt-Rosen ◽  
...  
Keyword(s):  
High Throughput ◽  
Spatial Organization ◽  
Spatially Resolved ◽  
Automated Platform

AbstractThe spatial organization of cells and molecules plays a key role in tissue function in homeostasis and disease. Spatial Transcriptomics (ST) has recently emerged as a key technique to capture and positionally barcode RNAs directly in tissues. Here, we advance the application of ST at scale, by presenting Spatial Multiomics (SM-Omics) as a fully automated high-throughput platform for combined and spatially resolved transcriptomics and antibody-based proteomics.

scholarly journals High-throughput mapping of long-range neuronal projection using in situ sequencing

2018 ◽  
Author(s):  
Xiaoyin Chen ◽  
Yu-Chi Sun ◽  
Huiqing Zhan ◽  
Justus M Kebschull ◽  
Stephan Fischer ◽  
...  
Keyword(s):  
High Throughput ◽  
Spatial Organization ◽  
Neural Circuits ◽  
Cell Types ◽  
Projection Neurons ◽  
Spatially Resolved ◽  
Neuronal Projection ◽  
Depth Analysis ◽  
Organizing Principles

SummaryUnderstanding neural circuits requires deciphering interactions among myriad cell types defined by spatial organization, connectivity, gene expression, and other properties. Resolving these cell types requires both single neuron resolution and high throughput, a challenging combination with conventional methods. Here we introduce BARseq, a multiplexed method based on RNA barcoding for mapping projections of thousands of spatially resolved neurons in a single brain, and relating those projections to other properties such as gene or Cre expression. Mapping the projections to 11 areas of 3579 neurons in mouse auditory cortex using BARseq confirmed the laminar organization of the three top classes (IT, PT-like and CT) of projection neurons. In depth analysis uncovered a novel projection type restricted almost exclusively to transcriptionally-defined subtypes of IT neurons. By bridging anatomical and transcriptomic approaches at cellular resolution with high throughput, BARseq can potentially uncover the organizing principles underlying the structure and formation of neural circuits.

scholarly journals A High-throughput Automated Platform for the Development of Manufacturing Cell Lines for Protein Therapeutics

10.3791/3010 ◽  
2011 ◽  
Author(s):  
Shuangping Shi ◽  
Russ G.G. Condon ◽  
Liang Deng ◽  
Jason Saunders ◽  
Finn Hung ◽  
...  
Keyword(s):  
Cell Lines ◽  
High Throughput ◽  
Protein Therapeutics ◽  
Manufacturing Cell ◽  
Automated Platform

scholarly journals A Fully Automated High-Throughput Flow Cytometry Screening System Enabling Phenotypic Drug Discovery

2018 ◽  
Vol 23 (7) ◽  
pp. 697-707 ◽  
Author(s):  
John Joslin ◽  
James Gilligan ◽  
Paul Anderson ◽  
Catherine Garcia ◽  
Orzala Sharif ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Screening System ◽  
Preclinical Development ◽  
Drug Identification ◽  
Therapeutic Setting ◽  
Compound Libraries ◽  
Automated Platform

The goal of high-throughput screening is to enable screening of compound libraries in an automated manner to identify quality starting points for optimization. This often involves screening a large diversity of compounds in an assay that preserves a connection to the disease pathology. Phenotypic screening is a powerful tool for drug identification, in that assays can be run without prior understanding of the target and with primary cells that closely mimic the therapeutic setting. Advanced automation and high-content imaging have enabled many complex assays, but these are still relatively slow and low throughput. To address this limitation, we have developed an automated workflow that is dedicated to processing complex phenotypic assays for flow cytometry. The system can achieve a throughput of 50,000 wells per day, resulting in a fully automated platform that enables robust phenotypic drug discovery. Over the past 5 years, this screening system has been used for a variety of drug discovery programs, across many disease areas, with many molecules advancing quickly into preclinical development and into the clinic. This report will highlight a diversity of approaches that automated flow cytometry has enabled for phenotypic drug discovery.

Automated High-Throughput System Combining Small-Scale Synthesis with Bioassays and Reaction Screening

2021 ◽  
pp. 247263032110478
Author(s):  
Nicolás M. Morato ◽  
MyPhuong T. Le ◽  
Dylan T. Holden ◽  
R. Graham Cooks
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Desorption Electrospray Ionization ◽  
Ambient Ionization ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Label Free ◽  
Minimal Sample ◽  
Secondary Droplets ◽  
Automated Platform

The Purdue Make It system is a unique automated platform capable of small-scale in situ synthesis, screening small-molecule reactions, and performing direct label-free bioassays. The platform is based on desorption electrospray ionization (DESI), an ambient ionization method that allows for minimal sample workup and is capable of accelerating reactions in secondary droplets, thus conferring unique advantages compared with other high-throughput screening technologies. By combining DESI with liquid handling robotics, the system achieves throughputs of more than 1 sample/s, handling up to 6144 samples in a single run. As little as 100 fmol/spot of analyte is required to perform both initial analysis by mass spectrometry (MS) and further MSn structural characterization. The data obtained are processed using custom software so that results are easily visualized as interactive heatmaps of reaction plates based on the peak intensities of m/ z values of interest. In this paper, we review the system’s capabilities as described in previous publications and demonstrate its utilization in two new high-throughput campaigns: (1) the screening of 188 unique combinatorial reactions (24 reaction types, 188 unique reaction mixtures) to determine reactivity trends and (2) label-free studies of the nicotinamide N-methyltransferase enzyme directly from the bioassay buffer. The system’s versatility holds promise for several future directions, including the collection of secondary droplets containing the products from successful reaction screening measurements, the development of machine learning algorithms using data collected from compound library screening, and the adaption of a variety of relevant bioassays to high-throughput MS.

scholarly journals SpatialDB: a database for spatially resolved transcriptomes

2019 ◽  
Author(s):  
Zhen Fan ◽  
Runsheng Chen ◽  
Xiaowei Chen
Keyword(s):  
Spatial Organization ◽  
Expression Patterns ◽  
Functional Enrichment ◽  
Web Interface ◽  
Current Version ◽  
Transcriptomic Data ◽  
Spatially Resolved ◽  
User Friendly ◽  
New Strategies

Abstract Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.

Simultaneous detection of multiple viruses based on chemiluminescence and magnetic separation

2017 ◽  
Vol 5 (1) ◽  
pp. 57-66 ◽  
Author(s):  
Zeeshan Ali ◽  
Jiuhai Wang ◽  
Yongjun Tang ◽  
Bin Liu ◽  
Nongyue He ◽  
...  
Keyword(s):  
Magnetic Nanoparticles ◽  
High Throughput ◽  
Dna Hybridization ◽  
Detection Method ◽  
Simultaneous Detection ◽  
Reliable Detection ◽  
Automated Platform ◽  
Modified Magnetic Nanoparticles ◽  
Surface Modified ◽  
Multiple Pathogens

In this report, a DNA hybridization based chemiluminescent detection method has been proposed for reliable detection of multiple pathogens. The use of surface modified magnetic nanoparticles can help to integrate this system into an automated platform for high throughput applications.

scholarly journals Construction of membrane-bound artificial cells using microfluidics: a new frontier in bottom-up synthetic biology

2016 ◽  
Vol 44 (3) ◽  
pp. 723-730 ◽  
Author(s):  
Yuval Elani
Keyword(s):  
Synthetic Biology ◽  
High Throughput ◽  
Lipid Membranes ◽  
Spatial Organization ◽  
Building Blocks ◽  
Artificial Cells ◽  
Bottom Up ◽  
Grand Challenges ◽  
New Frontier ◽  
Membrane Bound

The quest to construct artificial cells from the bottom-up using simple building blocks has received much attention over recent decades and is one of the grand challenges in synthetic biology. Cell mimics that are encapsulated by lipid membranes are a particularly powerful class of artificial cells due to their biocompatibility and the ability to reconstitute biological machinery within them. One of the key obstacles in the field centres on the following: how can membrane-based artificial cells be generated in a controlled way and in high-throughput? In particular, how can they be constructed to have precisely defined parameters including size, biomolecular composition and spatial organization? Microfluidic generation strategies have proved instrumental in addressing these questions. This article will outline some of the major principles underpinning membrane-based artificial cells and their construction using microfluidics, and will detail some recent landmarks that have been achieved.

scholarly journals Robust Acquisition of High Resolution Spatial Transcriptomes from Preserved Tissues with Immunofluorescence Based Laser Capture Microdissection

2021 ◽  
Author(s):  
Xiaodan Zhang ◽  
Chuansheng Hu ◽  
Chen Huang ◽  
Ying Wei ◽  
Xiaowei Li ◽  
...  
Keyword(s):  
Laser Capture Microdissection ◽  
Spatial Organization ◽  
Spatial Information ◽  
A Priori ◽  
Rna Degradation ◽  
Spatially Resolved ◽  
Mouse Small Intestine ◽  
Dissociated Cells ◽  
Laser Capture

The functioning of tissues is fundamentally dependent upon not only the phenotypes of the constituent cells but also their spatial organization in the tissue. However, obtaining comprehensive transcriptomic data based on established phenotypes while retaining this spatial information has been challenging. Here we present a general and robust method based on immunofluorescence-guided laser capture microdissection (immuno-LCM-RNAseq) to enable acquisition of finely resolved spatial transcriptomes with as few as tens of cells from snap-frozen or RNAlater-treated tissues, overcoming the long-standing problem of significant RNA degradation during this lengthy process. The efficacy of this approach is exemplified by the characterization of differences at the transcript isoform level between cells at the tip versus the main capillary body of the mouse small intestine lacteal. With the extensive repertoire of phenotype-specific antibodies that are presently available, our method provides a powerful means by which spatially resolved cellular states can be delineated in situ with preserved tissues. Moreover, such high quality spatial transcriptomes defined by immuno-markers can be used to compare with clusters obtained from single-cell RNAseq studies of dissociated cells as well as applied to bead-based spatial transcriptomics approaches that require such information a priori for cell identification.

scholarly journals stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics

2021 ◽  
Author(s):  
Shengquan Chen ◽  
Boheng Zhang ◽  
Xiaoyang Chen ◽  
Xuegong Zhang ◽  
Rui Jiang
Keyword(s):  
Gene Expression ◽  
Spatial Organization ◽  
Correlation Coefficients ◽  
Detection Sensitivity ◽  
Gene Detection ◽  
Spatially Resolved ◽  
Expression Of Genes ◽  
Number Of Genes ◽  
Joint Embedding ◽  
Spatial Cell

Motivation: Single-cell RNA sequencing (scRNA-seq) techniques have revolutionized the investigation of transcriptomic landscape in individual cells. Recent advancements in spatial transcriptomic technologies further enable gene expression profiling and spatial organization mapping of cells simultaneously. Among the technologies, imaging-based methods can offer higher spatial resolutions, while they are limited by either the small number of genes imaged or the low gene detection sensitivity. Although several methods have been proposed for enhancing spatially resolved transcriptomics, inadequate accuracy of gene expression prediction and insufficient ability of cell-population identification still impede the applications of these methods. Results: We propose stPlus, a reference-based method that leverages information in scRNA-seq data to enhance spatial transcriptomics. Based on an auto-encoder with a carefully tailored loss function, stPlus performs joint embedding and predicts spatial gene expression via a weighted k-NN. stPlus outperforms baseline methods with higher gene-wise and cell-wise Spearman correlation coefficients. We also introduce a clustering-based approach to assess the enhancement performance systematically. Using the data enhanced by stPlus, cell populations can be better identified than using the measured data. The predicted expression of genes unique to scRNA-seq data can also well characterize spatial cell heterogeneity. Besides, stPlus is robust and scalable to datasets of diverse gene detection sensitivity levels, sample sizes, and number of spatially measured genes. We anticipate stPlus will facilitate the analysis of spatial transcriptomics. Availability: stPlus with detailed documents is freely accessible at http://health.tsinghua.edu.cn/software/stPlus/ and the source code is openly available on https://github.com/xy-chen16/stPlus.

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