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Run your analysis now →The scope here covers 36 published records filed between 2015 and mid-2026 that combine single-cell or droplet-barcoding sequencing language with claim elements around cell capture efficiency, doublet rate, ambient RNA, library complexity or multiomic readout, classified under C12Q1, B01L3 or C12N15. That is a narrow, claim-language-anchored slice of a much larger single-cell literature, built to surface the assignees and sub-branches actually contesting these specific technical problems rather than every sequencing-adjacent filing. Filing rose sharply through the late 2010s, peaked in 2021, and has since pulled back — a pattern consistent with an early land-grab phase giving way to consolidation around a smaller set of dominant claim positions.
Publication lags filing by roughly 18 months, so the 2025 and 2026 counts in the trend chart understate real filing activity in those years; treat the recent-year drop as partly an artefact of the data cut-off, not solely a market signal.
The two views below cover the same 36 records from different angles: one tracks when the work was filed, the other tracks what technical ground it claims.
Activity climbed from 2 records in 2017 to a peak of 18 in 2021, then declined toward 3 at the 2022 midpoint and lower still afterward, partly reflecting publication lag near the 2026-07-31 cut-off.
C12Q covers every one of the 36 records in scope by construction of the search; C12N appears in 33.3% of records, G01N in 13.9%, C40B in 8.3%, and B01L and G06F each in 2.8% — since records carry multiple classes, these shares sum to more than 100%.
Shares are the percentage of the 36 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
This page is one run against one query. Ask Eureka your own question about single-cell sequencing technology and every answer comes back with the patent numbers behind it.
Try EurekaThe filing covers methods of performing single-cell RNA sequencing, methods of designing or providing a composition for that sequencing, and methods of analysing the resulting data. Its core technical move is a plurality of target-specific capture reagents, each tied to a respective target transcript, where the concentration of at least a first capture reagent differs from others in the set — a targeted-enrichment approach to sequencing library composition rather than whole-transcriptome capture.Abstract trimmed for length; concentration-differentiation detail is the operative claim element.


| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20170260584A1 | Cell population analysis using single nucleotide polymorphisms from single cell transcriptomes | 264 |
| 2 | WO2020190509A1 | Methods for using spatial arrays for single cell sequencing | 163 |
| 3 | WO2018172726A1 | Single cell DNA sequencing | 158 |
| 4 | US20220145361A1 | Methods for using spatial arrays for single cell sequencing | 150 |
| 5 | WO2017139690A1 | Cell population analysis using single nucleotide polymorphisms from single cell transcriptomes | 87 |
| 6 | US20230212656A1 | Methods of spatially resolved single cell sequencing | 20 |
| 7 | US20200347449A1 | Methods for determining spatial and temporal gene expression dynamics during adult neurogenesis in single cel… | 19 |
| 8 | WO2021188838A1 | Single-cell combinatorial indexed cytometry sequencing | 13 |
| 9 | WO2021168455A1 | Methods of spatially resolved single cell RNA sequencing | 12 |
| 10 | WO2020190509A9 | Methods for using spatial arrays for single cell sequencing | 10 |
Citation counts reward older filings that have had more time to accumulate citations within this searched corpus; read them as a signal of influence on the field, not of current commercial relevance.
Each row carries its publication number; clicking a row searches Eureka by that number.
When you want the answer in the next five minutes.
The agent works the prompt against patents and technical literature, citing every source.
Run your analysis now →When it has to run inside your own pipeline.
Patent search, landscape analysis and assignee resolution as MCP tools. Drop them into any agent framework, or call REST directly.
Browse MCP servers →Three patterns in this dataset matter more than the raw counts: where citations concentrate, how the technology mix skews, and how flat the recent trend really is.
The most-cited record, US20170260584A1 on cell population analysis from single-cell transcriptome SNPs, carries 264 citations — well ahead of the next tier. Two of the five most-cited records share the same underlying spatial-array method described in different filings, suggesting a single technical approach re-filed across jurisdictions or continuations rather than five independent breakthroughs.
Every record in scope touches C12Q measurement and testing claims by design of the search, but only 33.3% also carry C12N genetic-engineering classification, and single digits touch G01N material analysis, C40B combinatorial libraries, or B01L lab apparatus. That gap suggests the apparatus and library-construction side of single-cell workflows is claimed far less densely than the measurement and analysis side.
Filing rose from 2 records in 2017 to 18 in 2021, then dropped to 3 by the 2022 midpoint and lower afterward. None of the leading assignees show any filings in the latest year of the dataset, which is consistent with the broader pullback rather than any single company exiting the space.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to single-cell sequencing technology, with the prior art for and against each one.
The ranking below covers the 12 companies the data endpoint returns for this search — not a top-50 or top-100 list — and it is heavily front-loaded.
The top-ranked assignee in this set holds 22 records, more than six times the fifth-place count of 3 and over twenty times the tenth-place count of 1. That gap is steep enough that a newcomer's freedom-to-operate analysis should start with this assignee's portfolio before looking anywhere else in the ranking.
Ten distinct co-assignee pairs appear in the dataset, and the strongest pair shares 11 records together — evidence of a sustained joint-filing relationship rather than one-off collaboration. A second pairing shares 3 records and a third shares 2, so co-filing activity outside the lead pair is thin.
Every one of the six assignees tracked for recent-year momentum, including the portfolio leader, registers zero filings in the latest year of the dataset. Given the roughly 18-month publication lag, this likely reflects filings still working through the pipeline rather than a genuine stop in R&D.
| Assignee | Recent year | YoY |
|---|---|---|
| The Regents of the University of California | 0 | — |
| Chan Zuckerberg Biohub, Inc. | 0 | — |
| 10x Genomics, Inc. | 0 | — |
| Fred Hutchinson Cancer Research Center | 0 | — |
| President and Fellows of Harvard College | 0 | — |
| Cambridge Enterprise Limited | 0 | — |
| RAMACHANDRAN IYER ESWAR PRASAD | 0 | — |
| MIKKELSEN TARJEI SIGURD | 0 | — |
The dataset points to a few concrete next steps for anyone deciding whether and where to file.
With 22 of the ranked records, the top assignee's portfolio defines much of the available claim space in this search. A detailed claim-chart review of that portfolio is the fastest way to find out what is actually blocked before drafting new claims.
Explore assignee portfolios in EurekaThe strongest co-assignee pair shares 11 records, a pattern worth tracking for licensing or acquisition signals rather than treating the two organisations as independent filers.
Trace co-assignee relationships in EurekaBecause publication lags filing by about 18 months, the 2025-2026 filing counts here are still incomplete. A follow-up pull in twelve months will give a truer read on whether the 2021 peak was a one-off or the start of a new cycle.
Set a landscape refresh in EurekaIn this dataset of 36 records, one assignee holds a clear lead with 22 records, well ahead of the fifth-place holder at 3 and the tenth-place holder at 1 among the 12 companies the ranking covers. That concentration means most of the claim space around the searched terms — cell capture efficiency, doublet rate, ambient RNA, library complexity and multiomic readout — sits with a small number of organisations, led by one. Anyone assessing freedom to operate should start there before reviewing the longer tail of single-digit filers.
Filing rose from 2 records in 2017 to a peak of 18 in 2021, then dropped back to 3 at the 2022 midpoint and lower in subsequent years through the 2026-07-31 cut-off. That decline should be read cautiously, because publication typically lags filing by around 18 months, so the most recent one to two years are undercounted in any trend chart. The 2021 peak is the most reliable signal in the series; whether filing has genuinely slowed or is simply pending publication will only be clear after a later data refresh.
Every one of the 36 records in scope carries a C12Q classification covering enzyme and DNA measurement and testing, which is expected given the search criteria. Beyond that baseline, 33.3% of records also carry C12N genetic-engineering classification, 13.9% carry G01N material analysis, and single-digit percentages touch C40B combinatorial libraries, B01L lab apparatus and G06F data processing. That skew suggests measurement and analysis methods are far more densely claimed than apparatus or library-construction chemistry.
The IPC composition points to apparatus and library-construction claims as comparatively open ground: B01L lab-apparatus classification appears in only 2.8% of the 36 records, and C40B combinatorial-library classification in just 8.3%, against 100% coverage of the core C12Q measurement claims. Doublet-rate correction, ambient RNA background modelling and multiomic-readout instrumentation are the specific technical themes named in the search criteria that show thin representation in the classification data. A first claim in these areas would likely face less prior art than one written around core transcriptome measurement methods.
WO2025040599A1, filed by Cambridge Enterprise Limited on 2025-02-27, covers methods of performing single-cell RNA sequencing using a plurality of target-specific capture reagents where the concentration of at least one reagent differs from the others in the set, along with related methods for designing such compositions and analysing the resulting sequencing data. The differentiated-concentration capture-reagent approach is its distinguishing technical element over generic single-cell library preparation. Anyone designing a targeted-enrichment single-cell workflow should review this filing's claim scope directly rather than relying on the abstract summary alone.
Go past this page: query the whole single-cell sequencing technology corpus yourself, in your own scope.
Every answer comes back with patent numbers you can open.
Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company's registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.
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