Submission declined on 11 December 2025 by WeirdNAnnoyed (talk). This draft's references do not show that the subject meets Wikipedia's criteria for inclusion. The draft requires multiple published secondary sources that: provide significant coverage: discuss the subject in detail, not just brief mentions or routine announcements; are reliable: from reputable outlets with editorial oversight; are independent: not connected to the subject, such as interviews, press releases, the subject's own website, or sponsored content. Please add references that meet all three of these criteria. If none exist, the subject is not yet suitable for Wikipedia. This draft appears to contain text generated by a large language model (such as ChatGPT). You cannot use LLMs to generate article content. LLM-generated pages with certain obvious signs of being machine generated may be deleted without notice. These tools are prone to specific issues that violate our policies: hallucinations: they often invent false information and cite non-existent references. unencyclopedic tone: they tend to be vague, promotional, or essay-like, rather than neutral and factual. copyright issues: they may closely paraphrase existing text, leading to copyright violations. Instead, only summarize in your own words a range of independent, reliable, published sources that discuss the subject. See the advice page on large language models for more information. If you would like to continue working on the submission, click on the "Edit" tab at the top of the window. If you have not resolved the issues listed above, your draft will be declined again and potentially deleted. If you need extra help, please ask us a question at the AfC Help Desk or get live help from experienced editors. Please do not remove reviewer comments or this notice until the submission is accepted. Where to get help If you need help editing or submitting your draft, please ask us a question at the AfC Help Desk or get live help from experienced editors. These venues are only for help with editing and the submission process, not to get reviews. If you need feedback on your draft, or if the review is taking a lot of time, you can try asking for help on the talk page of a relevant WikiProject. Some WikiProjects are more active than others so a speedy reply is not guaranteed. How to improve a draft Wikipedia:Contributing to Wikipedia – a basic overview on how to edit Wikipedia. Help:Wikitext – how to use the markup Help:Referencing for beginners – how to include references Wikipedia:Article development – how to develop your article Wikipedia:Writing better articles – how to improve your article Wikipedia:Verifiability – make sure your article includes reliable third-party sources You can also browse Wikipedia:Featured articles and Wikipedia:Good articles to find examples of Wikipedia's best writing on topics similar to your proposed article. Improving your odds of a speedy review To improve your odds of a faster review, tag your draft with relevant WikiProject tags using the button below. This will let reviewers know a new draft has been submitted in their area of interest. For instance, if you wrote about a female astronomer, you would want to add the Biography, Astronomy, and Women scientists tags. Add tags to your draft Editor resources Easy tools: Citation bot (help) | Advanced: Fix bare URLs Declined by WeirdNAnnoyed 5 months ago. Last edited by WeirdNAnnoyed 5 months ago. Reviewer: Inform author. Resubmit Please note that if the issues are not fixed, the draft will be declined again.

• Comment: I'm not convinced based on the sources that this variant of flow cytometry is notable. Yes, two of the sources are secondary, but with the explosion in the number of journals in the past decade, and the explosion in review articles in an effort to juice citation statistics, I'm not convinced a mere two review articles are sufficient these days (the third, primary source is barely cited). The article also contains a lot of fluff like the "spectral unmixing" subsection, which could apply to any multichannel spectral experiment. I suspect AI was used in the drafting or writing of this article as well. The text is too uniform and there are far too many bulleted lists where a sentence would suffice, and technical-sounding but ultimately meaningless passages like the last sentence of "instrumentation". I would suggest this be cut down signficantly (to 1-3 paragraphs) and added as a section to the main flow cytometry article, but if you'd like to resubmit, please condense the article, add sources, and convert the bullets to prose. WeirdNAnnoyed (talk) 12:53, 11 December 2025 (UTC)

Spectral flow cytometry (also: full spectrum flow cytometry) is a type of flow cytometry in which a cytometer collects the full fluorescence emission spectrum of each fluorochrome across multiple detectors rather than measuring discrete bandpass channels. The spectral data are computationally unmixed to determine the contribution of each fluorochrome and cellular autofluorescence, enabling high-parameter immunophenotyping with 30 or more markers per sample.^[1][2]

The terms spectral flow cytometry and full spectrum flow cytometry are used interchangeably in the scientific literature. “Full spectrum” emphasizes measurement of the complete emission profile rather than selected optical channels.^[2]

Early concepts of spectral fluorescence detection in flow cytometry were explored in the 1990s as part of efforts to expand measurable parameters beyond the constraints of filter-based optics.^[1] A key technical demonstration was published in 2015, showing that full-spectrum detection could distinguish multiple spectrally overlapping fluorescent proteins and conventional fluorochromes using computational unmixing.^[3]

Prototype full-spectrum instruments were later commercialized in the early 2010s, and advances in detector arrays, dispersive optics and unmixing algorithms enabled spectral cytometry to become a major direction in high-dimensional single-cell analysis throughout the 2010s and 2020s.^[1][2]

Conventional flow cytometry measures fluorescence using narrow bandpass filters. Spectral overlap necessitates compensation and limits multiplexing.

Spectral flow cytometry instead:

• disperses emitted light into tens of narrow spectral bins,
• records a full emission signature across an array of detectors,
• uses computational unmixing to determine fluorochrome contributions.

This approach enables:

• resolution of fluorochromes with highly overlapping spectra,^[1]
• improved performance in autofluorescent samples,
• expansion of high-parameter panels beyond conventional limits.^[1]

Spectral unmixing models the measured emission as a linear combination of reference spectra for each fluorochrome plus one or more autofluorescence components. Solving this system yields unmixed marker intensities.^[2]

Early experimental work demonstrated that full-spectrum acquisition could reliably distinguish multiple fluorescent proteins and conventional fluorochromes using spectral detection and unmixing, validating the feasibility of the method.^[3]

Successful unmixing requires:

• accurate and stable reference spectra,
• explicit modeling of autofluorescence,
• quality control to detect spectral drift or detector changes.^[1][2]

Spectral (full spectrum) cytometers typically employ:

• dispersive optics such as prisms or diffraction gratings,
• multi-channel detector arrays (PMT, APD or CMOS-based),
• software pipelines for spectral unmixing and quality control.

These instruments differ from traditional cytometers in that they do not rely on dedicated filters for each fluorochrome; instead, the entire emission pattern is used for identification.^[1]

Optical architecture, number of spectral channels and unmixing algorithms differ by implementation, but share the core concept of full-spectrum acquisition.

Several scientific instrument manufacturers have developed spectral (full-spectrum) cytometers. Peer-reviewed reviews describe these systems collectively as a class of high-parameter instruments employing spectral detection and computational unmixing.^[1][2]

Commercially described platforms include:

• spectral detectors with dispersive optics and multi-anode PMTs or APDs,
• instruments with integrated spectral unmixing pipelines,
• spectral cell sorters,
• compact analyzers for routine laboratory workflows.

Published literature references early commercial spectral systems in the 2010s and expanded platforms in the 2020s.^[1][2]

Spectral flow cytometry is used in:

• immunology and immune profiling,
• oncology and tumor microenvironment analysis,
• cell therapy research including CAR-T,
• infectious disease and inflammation studies,
• high-parameter T- and B-cell immunophenotyping,
• analysis of autofluorescent tissues.^[2]

It is commonly integrated with computational workflows such as:

• clustering (FlowSOM, PhenoGraph),
• dimensionality reduction (t-SNE, UMAP),
• machine-learning-based automated gating.^[1][2]

Clinical evaluations have shown that full-spectrum cytometry is promising for:

• diagnostic hematologic immunophenotyping,
• minimal residual disease (MRD) detection,
• immune monitoring,
• extended diagnostic panels with 20–30 or more markers.^[2]

Several spectral cytometers have received regulatory approval in clinical laboratories, and spectral unmixing workflows are increasingly being incorporated into diagnostic protocols.^[2]

• improved resolution of overlapping fluorochromes,^[1]
• better handling of autofluorescence,^[1]
• reduced reliance on complex filter sets,^[1]
• supports high-parameter panels (30+),^[1]
• flexible use of lasers and dyes.^[1]

• requires accurate and stable reference spectra,^[2]
• sensitive to spectral drift or optical changes,^[1]
• computationally intensive quality control and analysis,^[2]
• requires personnel trained in spectral data interpretation.^[1][2]

• Flow cytometry
• Fluorescence spectroscopy
• Single-cell analysis
• Immunophenotyping

• https://pubmed.ncbi.nlm.nih.gov/?term=spectral+flow+cytometry — PubMed search for "spectral flow cytometry"