Evidence map›Paper›PMID 36147664›Full record

ReviewComputational and structural biotechnology journal2022

Computational solutions for spatial transcriptomics.

Iivari Kleino, Paulina Frolovaitė, Tomi Suomi, Laura L Elo

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing papers in PubMed
10.4field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

65 citing papers in PubMed, 121 citations in OpenAlex.

  1. Insights Into Spatial Transcriptomics: Exploring Recent Technical Developments and Their Diverse Applications.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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  7. Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026
    Review
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  15. Article
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5 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Iivari KleinoTurku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland.
Paulina FrolovaitėTurku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland.
Tomi SuomiTurku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland.
Laura L EloTurku Bioscience Centre, University of Turku and Åbo Akademi University Turku, Turku, Finland.
Åbo Akademi University · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Indexed as

AOI, area of illuminationBaysor, Bayesian Segmentation of Spatial Transcriptomics DataBICCN, Brain Initiative Cell Census NetworkBinSpect, Binary Spatial ExtractionBOLORAMIS, barcoded oligonucleotides ligated on RNA amplified for multiplexed and parallel in situ analysesCCC, cell–cell communicationCCI, cell–cell interactionsCNV, copy-number variationComputational biologyDbiT-Seq, Deterministic Barcoding in Tissue for spatial omics sequencingDSP, digital spatial profilingFA, factor analysisFFPE, formalin-fixed, paraffin-embeddedFISH, fluorescence in situ hybridizationFISSEQ, fluorescence in situ sequencing of RNAFOV, Field of viewGRNs, gene regulation networksGSEA, gene set enrichment analysisGSVA, gene set variation analysisHDST, high definition spatial transcriptomicsHMRF, hidden Markov random fieldICG, interaction changed genesISH, in situ hybridizationISS, in situ sequencingJSTA, Joint cell segmentation and cell type annotationKNN, k-nearest neighborLCM, Laser Capture MicrodissectionLCM-seq, laser capture microdissection coupled with RNA sequencingLOH, loss of heterozygosity analysisMC, Molecular CartographyMERFISH, multiplexed error-robust FISHNMF (NNMF), Non-negative matrix factorizationPCA, Principal Component AnalysisPIXEL-seq, Polony (or DNA cluster)-indexed library-sequencingPL-lig, padlock ligationQC, quality controlRNAseq, RNA sequencingROI, region of interestSCENIC, Single-Cell rEgulatory Network Inference and ClusteringscRNA-seq, single-cell RNA sequencingscvi-tools, single-cell variational inference toolsseqFISH, sequential fluorescence in situ hybridizationsequ-smFISH, sequential single-molecule fluorescent in situ hybridizationSingle-cell analysisSME, Spatial Morphological gene Expression normalizationsmFISH, single molecule FISHSPATA, SPAtial Transcriptomic AnalysisSpatial data analysis frameworksSpatial deconvolutionSpatial transcriptomicsSTARmap, spatially-resolved transcript amplicon readout mappingST Pipeline, Spatial Transcriptomics PipelineST, Spatial transcriptomicsTIVA, Transcriptome in Vivo AnalysisTMA, tissue microarrayTME, tumor micro environmentt-SNE, t-distributed stochastic neighbor embeddingUMAP, Uniform Manifold Approximation and Projection for Dimension ReductionUMI, unique molecular identifierZipSeq, zipcoded sequencing.

Identifiers

PMID36147664
PMCPMC9464853
OpenAlexW4294052977

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.