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Single-cell and spatial transcriptomics

Single-cell and spatial transcriptomics is a research topic within Molecular Biology. Science Explorer counts 23k research works in it since 1952. 25.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the comprehensive integration and analysis of single-cell transcriptomic data, covering topics such as cell types, spatial profiling, lineage tracking, data integration, gene expression, and cell heterogeneity. The research explores various technologies and computational methods to study the transcriptomic landscape at the single-cell level.

  • Single-Cell
  • Transcriptomics
  • RNA-Seq
  • Cell Types
  • Spatial Profiling
  • Lineage Tracking
  • Data Integration
  • Gene Expression
  • Cell Heterogeneity
  • Droplet-based Sequencing
Research works
23k
fractional, since 1952
In the world top 10%
6k
per year above
Top-10% rate
25.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+177%
the tick is no change

Which countries lead Single-cell and spatial transcriptomics research?

By volume, the United States and China publish the most (3.1k and 2.9k works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 3.1k works
  2. 2 China 2.9k works
  3. 3 Germany 471 works
  4. 4 United Kingdom 390 works
  5. 5 France 219 works
  6. 6 Canada 216 works
  7. 7 Japan 206 works
  8. 8 Australia 190 works
  9. 9 South Korea 166 works
  10. 10 Italy 152 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 39.1%China: 35.9%Germany: 5.9%United Kingdom: 4.8%6 others listed: 14.3%39%largest
United States3,147 · 39.1%China2,893 · 35.9%Germany471 · 5.9%United Kingdom390 · 4.8%6 others listed1,149 · 14.3%

Shares of the rows listed above, not of the whole node.

Which institutions lead Single-cell and spatial transcriptomics research?

By volume in 2022–2025, Stanford University publishes the most Single-cell and spatial transcriptomics research, followed by Harvard University and Sun Yat-sen University.

By volume, 2022–2025

  1. 1 Stanford University United States 68 works
  2. 2 Harvard University United States 66 works
  3. 3 Sun Yat-sen University China 62 works
  4. 4 University of North Carolina at Chapel Hill United States 54 works
  5. 5 Chinese Academy of Medical Sciences & Peking Union Medical College China 53 works
  6. 6 Shanghai Jiao Tong University China 48 works
  7. 7 The University of Texas MD Anderson Cancer Center United States 48 works
  8. 8 University of Pennsylvania United States 48 works
  9. 9 Peking University China 44 works
  10. 10 University of Michigan United States 44 works

Who are the leading researchers in Single-cell and spatial transcriptomics?

The most-cited researchers publishing on Single-cell and spatial transcriptomics include Benjamin L. Ebert and Gad Getz.

  1. 1 Benjamin L. Ebert 6.7k citations
  2. 2 Gad Getz 6.2k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Single-cell and spatial transcriptomics research done?

The largest centres of Single-cell and spatial transcriptomics research in 2022–2025 are Beijing (China), Shanghai (China), Guangzhou (China) and New York (United States).

Largest cities, 2022–2025

  1. 1 Beijing China 446 works
  2. 2 Shanghai China 310 works
  3. 3 Guangzhou China 235 works
  4. 4 New York United States 173 works
  5. 5 Cambridge United States 160 works
  6. 6 Boston United States 142 works
  7. 7 London United Kingdom 128 works
  8. 8 Hangzhou China 127 works
  9. 9 Paris France 117 works
  10. 10 Nanjing China 117 works
See Single-cell and spatial transcriptomics on the map

Where is the best place to study Single-cell and spatial transcriptomics?

Among universities, judged by research, Karolinska Institutet, Weizmann Institute of Science and Harvard University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 36.6%fractional works in this node (log) →share in the world top 10% →Karolinska Institutet: 35, 34.0%Weizmann Institute of Science: 18, 36.0%Harvard University: 66, 40.5%California Institute of Technology: 14, 50.2%Center for Life Sciences: 12, 45.1%Washington University in St. Louis: 38, 29.5%Stanford University: 68, 31.0%University of Pennsylvania: 48, 33.3%Yale University: 43, 30.1%Peking University: 44, 36.3%California Institute…Harvard UniversityWeizmann Institute o…Karolinska Institutet
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1Karolinska Institutet Sweden 74.634.0%8.8×35 +220.8%
2Weizmann Institute of Science Israel 68.936.0%13.5×18 +181.1%
3Harvard University United States 67.240.5%5.9×66 +106.5%
4California Institute of Technology United States 62.050.2%6.6×14 +167.9%
5Center for Life Sciences China 61.145.1%25.5×12
6Washington University in St. Louis United States 59.829.5%6.0×38 +295.2%
7Stanford University United States 59.431.0%6.2×68 +64.2%
8University of Pennsylvania United States 58.733.3%5.8×48 +118.5%
9Yale University United States 57.330.1%4.8×43 +177.5%
10Peking University China 57.236.3%3.1×44 +218.3%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Single-cell and spatial transcriptomics research growing?

Output in 2018–2022 was 177% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Single-cell and spatial transcriptomics.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.