Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Rodriques, Samuel G.; Stickels, Robert R.; Goeva, Aleksandrina; Martin, Clay; Murray, Evan; Vanderburg, Charles R.; Welch, Joshua D.; Chen, Linlin M.; Chen, Fei; Macosko, Evan Z.

doi:10.1126/science.aaw1219

Cited by 1,746 publications

(1,454 citation statements)

References 37 publications

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“…There are now many techniques and models to study astrocytes, some mentioned in the previous paragraphs [e.g., new transgenic mice, purification methods, and human induced pluripotent stem cells (iPSCs)derived astrocytes (Almad & Maragakis, 2018;Guttenplan & Liddelow, 2019)]. In addition, refined cellular and molecular methods developed in other fields could prove very useful to study reactive astrocytes like spatial transcriptomics (Rodriques et al, 2019;Wang et al, 2018), scRNAseq (Svensson et al, 2018), Cas9 genome editing and screening (Shalem, Sanjana, & Zhang, 2015), and multiplexed immunostainings (Goltsev et al, 2018). Miniaturization (in terms of quantity of input sample), extension to large brain regions and multiplexing to hundreds or thousands of target genes or proteins will achieve unprecedented resolution to decipher how astrocytes react in a given disease context and define better molecular markers (see Section 6).…”

Section: Which New Approaches and Tools Will Move The Field Forward?mentioning

confidence: 99%

Questions and (some) answers on reactive astrocytes

Escartin

Guillemaud

Sauvage

2019

Glia

232

180

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Astrocytes are key cellular partners for neurons in the central nervous system. Astrocytes react to virtually all types of pathological alterations in brain homeostasis by significant morphological and molecular changes. This response was classically viewed as stereotypical and is called astrogliosis or astrocyte reactivity. It was long considered as a nonspecific, secondary reaction to pathological conditions, offering no clues on disease‐causing mechanisms and with little therapeutic value. However, many studies over the last 30 years have underlined the crucial and active roles played by astrocytes in physiology, ranging from metabolic support, synapse maturation, and pruning to fine regulation of synaptic transmission. This prompted researchers to explore how these new astrocyte functions were changed in disease, and they reported alterations in many of them (sometimes beneficial, mostly deleterious). More recently, cell‐specific transcriptomics revealed that astrocytes undergo massive changes in gene expression when they become reactive. This observation further stressed that reactive astrocytes may be very different from normal, nonreactive astrocytes and could influence disease outcomes. To make the picture even more complex, both normal and reactive astrocytes were shown to be molecularly and functionally heterogeneous. Very little is known about the specific roles that each subtype of reactive astrocytes may play in different disease contexts. In this review, we have interrogated researchers in the field to identify and discuss points of consensus and controversies about reactive astrocytes, starting with their very name. We then present the emerging knowledge on these cells and future challenges in this field.

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Section: Which New Approaches and Tools Will Move The Field Forward?mentioning

confidence: 99%

Questions and (some) answers on reactive astrocytes

Escartin

Guillemaud

Sauvage

2019

Glia

232

180

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“…A major challenge in the analysis of scRNA-seq data is the scalability of analysis methods as datasets increase in size over time. This is particularly problematic as experiments now frequently produce millions of cells [48,49,50,51], making it challenging to even load the data into memory. Providing analysis methods, such as unsupervised clustering, that do not require data to be loaded into memory is an imperative step for scalable analyses.…”

Section: Availability and Future Directionsmentioning

confidence: 99%

mbkmeans: fast clustering for single cell data using mini-batchk-means

Hicks

Liu

et al. 2020

Preprint

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Single-cell RNA-Sequencing (scRNA-seq) is the most widely used high-throughput technology to measure genome-wide gene expression at the single-cell level. One of the most common analyses of scRNA-seq data detects distinct subpopulations of cells through the use of unsupervised clustering algorithms. However, recent advances in scRNA-seq technologies result in current datasets ranging from thousands to millions of cells. Popular clustering algorithms, such as k-means, typically require the data to be loaded entirely into memory and therefore can be slow or impossible to run with large datasets. To address this problem, we developed the mbkmeans R/Bioconductor package, an open-source implementation of the mini-batch k-means algorithm. Our package allows for on-disk data representations, such as the common HDF5 file format widely used for single-cell data, that do not require all the data to be loaded into memory at one time. We demonstrate the performance of the mbkmeans package using large datasets, including one with 1.3 million cells. We also highlight and compare the computing performance of mbkmeans against the standard implementation of k - means. Our software package is available in Bioconductor at https://bioconductor.org/packages/mbkmeans.

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“…To generate fulllength cDNA reads, we decoupled imaging from sequencing by reading transcript-associated barcodes in situ, followed by cDNA amplicon extraction and Next-Generation Sequencing (NGS). The shared barcode sequences are then used to map assembled NGS reads to a reference tissue atlas bearing barcode coordinates (14,22).…”

Section: Correspondence: Jlee@cshledumentioning

confidence: 99%

“…Multiple methods can measure the transcript abundance in situ in situ (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22). Yet, it remains difficult to spatially map regulatory elements and RBPs in a systematic and unbiased manner.…”

Section: Correspondence: Jlee@cshledumentioning

confidence: 99%

In Situ Transcriptome Accessibility Sequencing (INSTA-seq)

Fürth

Hatini

Lee

2019

Preprint

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Subcellular RNA localization regulates spatially polarized cellular processes, but unbiased investigation of its control in vivo remains challenging. Current hybridization-based methods cannot differentiate small regulatory variants, while in situ sequencing is limited by short reads. We solved these problems using a bidirectional sequencing chemistry to efficiently image transcript-specific barcode in situ, which are then extracted and assembled into longer reads using NGS. In the Drosophila retina, genes regulating eye development and cytoskeletal organization were enriched compared to methods using extracted RNA. We therefore named our method In Situ Transcriptome Accessibility sequencing (INSTA-seq). Sequencing reads terminated near 3' UTR cis-motifs (e.g. Zip48C, stau), revealing RNAprotein interactions. Additionally, Act5C polyadenylation isoforms retaining zipcode motifs were selectively localized to the optical stalk, consistent with their biology. Our platform provides a powerful way to visualize any RNA variants or protein interactions in situ to study their regulation in animal development.

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Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Cited by 1,746 publications

References 37 publications

Questions and (some) answers on reactive astrocytes

Questions and (some) answers on reactive astrocytes

mbkmeans: fast clustering for single cell data using mini-batchk-means

In Situ Transcriptome Accessibility Sequencing (INSTA-seq)

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