QSpec: online control and data analysis system for single-cell Raman spectroscopy
Open accessAbstract
Single-cell phenotyping is critical to the success of biological reductionism. Raman-activated cell sorting (RACS) has shown promise in resolving the dynamics of living cells at the individual level and to uncover population heterogeneities in comparison to established approaches such as fluorescence-activated cell sorting (FACS). Given that the number of single-cells would be massive in any experiment, the power of Raman profiling technique for single-cell analysis would be fully utilized only when coupled with a high-throughput and intelligent process control and data analysis system. In this work, we established QSpec, an automatic system that supports high-throughput Raman-based single-cell phenotyping. Additionally, a single-cell Raman profile database has been established upon which data-mining could be applied to discover the heterogeneity among single-cells under different conditions. To test the effectiveness of this control and data analysis system, a sub-system was also developed to simulate the phenotypes of single-cells as well as the device features.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Databases of single-cell bacteria, yeast, and microalgae spectra can all be used together to improve prediction and classification models of microalgae.
“Furthermore, databases of single-cell bacteria, yeast, and microalgae spectra can all be used together to improve prediction and classification models of microalgae [30].”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [30]
The Results sections on the Raman profile database and profile comparison describe a database containing bacterial, yeast, and microalgal spectra and report classification of Nannochloropsis cells against it. They do not show that combining those groups improves microalgae prediction or classification models relative to using microalgal spectra alone. Page numbers are unavailable in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredContinued data collection of microalgae spectra into spectral databases can improve machine learning models (e.g. SVM) to become more robust and applied to MVPC.
“Moreover, with continued data collection of microalgae spectra into spectral databases, machine learning models (e.g. SVM) can be improved to become more robust [30] and applied to MVPC.”
From Microalgal process-monitoring based on high-selectivity spectroscopy tools: status and future perspectives (2018) · cites this paper as [30]
The Results section, “Single-cell Raman profile comparison” (page unavailable), reports using SVM to search a Raman-spectra database that includes microalgae and finding higher accuracy than Euclidean Distance. It examines database size, but does not establish that continued collection makes SVM models more robust. The application to MVPC appears after the citation and is not treated as a claim attributed to this source. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Phylogenetic identification and in situ detection of individual microbial cells without cultivation
Amann, R., Ludwig, W., & Schleifer, K. (1995) · Microbiological reviews · doi:10.1128/mr.59.1.143-169.1995 · Cited by 9,508 · see citing works
Phylogenetic identification and in situ detection of individual microbial cells without cultivation.
Amann, R., Ludwig, W., & Schleifer, K. H. (1995) · Microbiological Reviews · doi:10.1128/mmbr.59.1.143-169.1995 · Cited by 4,244 · see citing works
Danielsson, P. (1980) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(80)90054-4 · Cited by 2,132 · see citing works
The Uncultured Microbial Majority
Rappé, M. S., & Giovannoni, S. J. (2003) · Annual Review of Microbiology · doi:10.1146/annurev.micro.57.030502.090759 · Cited by 2,017 · see citing works
Bacterial Rhodopsin: Evidence for a New Type of Phototrophy in the Sea
Béjà, O., Aravind, L., Koonin, E. V., Suzuki, M. T., Hadd, A. G., Nguyen, L. P., Jovanovich, S. B., Gates, C. M., Feldman, R. A., Spudich, J. L., et al. (2000) · Science · doi:10.1126/science.289.5486.1902 · Cited by 1,532 · see citing works
Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell
Zong, C., Lu, S., Chapman, A. R., & Xie, X. S. (2012) · Science · doi:10.1126/science.1229164 · Cited by 1,154 · see citing works
Introductory Raman Spectroscopy
(2003) · Elsevier eBooks · doi:10.1016/b978-0-12-254105-6.x5000-8 · Cited by 991 · see citing works
Microfluidic sorting of mammalian cells by optical force switching
Wang, M. M., Tu, E. L., Raymond, D. E., et al. (2004) · Nature Biotechnology · doi:10.1038/nbt1050 · Cited by 692 · see citing works
Winter forest soil respiration controlled by climate and microbial community composition
Monson, R. K., Lipson, D. L., Burns, S. P., Turnipseed, A. A., Delany, A. C., Williams, M. W., & Schmidt, S. K. (2006) · Nature · doi:10.1038/nature04555 · Cited by 542 · see citing works
Handbook of Raman Spectroscopy
Lewis, I. R., & Edwards, H. G. M. (2001) · CRC Press eBooks · doi:10.1201/9781420029253 · Cited by 529 · see citing works
Handbook of raman spectroscopy from the research laboratory to the process line
Lewis, I. R., & Edwards, H. G. M. (2001) · Cited by 468 · see citing works
Raman Microscopic Analysis of Single Microbial Cells
Huang, W. E., Griffiths, R. I., Thompson, I. P., Bailey, M., & Whiteley, A. S. (2004) · Analytical Chemistry · doi:10.1021/ac049753k · Cited by 411 · see citing works
Autotrophic growth of nitrifying community in an agricultural soil
Xia, W., Zhang, C., Zeng, X., et al. (2011) · The ISME Journal · doi:10.1038/ismej.2011.5 · Cited by 402 · see citing works
Wagner, M. (2009) · Annual Review of Microbiology · doi:10.1146/annurev.micro.091208.073233 · Cited by 264 · see citing works
Single cell Raman spectroscopy for cell sorting and imaging
Li, M., Xu, J., Romero-González, M. E., Banwart, S. A., & Huang, W. E. (2011) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2011.11.019 · Cited by 250 · see citing works
Self-citationNeighborhood coding of binary images for fast contour following and general binary array processing
Sobel, I. (1978) · Computer Graphics and Image Processing · doi:10.1016/s0146-664x(78)80020-3 · Cited by 246 · see citing works
Tumors on chips: oncology meets microfluidics
Włodkowic, D., & Cooper, J. M. (2010) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2010.08.016 · Cited by 179 · see citing works
Amantonico, A., Urban, P. L., Fagerer, S. R., et al. (2010) · Analytical Chemistry · doi:10.1021/ac1015326 · Cited by 145 · see citing works
Raman tweezers sorting of single microbial cells
Huang, W. E., Ward, A. D., & Whiteley, A. S. (2009) · Environmental Microbiology Reports · doi:10.1111/j.1758-2229.2008.00002.x · Cited by 139 · see citing works
Raman tweezers and their application to the study of singly trapped eukaryotic cells
Snook, R. D., Harvey, T. J., Faria, E. C., & Gardner, P. (2008) · Integrative Biology · doi:10.1039/b815253e · Cited by 93 · see citing works
Microfabricated analytical systems for integrated cancer cytomics
Włodkowic, D., & Cooper, J. M. (2010) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-010-3722-8 · Cited by 51 · see citing works
Pascut, F. C., Notingher, I., Goh, H. T., et al. (2011) · Journal of Biomedical Optics · doi:10.1117/1.3570302 · Cited by 49 · see citing works
Multianalyte Single-Cell Analysis with Multiple Cell Lines Using a Fiber-Optic Array
Whitaker, R. D., & Walt, D. R. (2007) · Analytical Chemistry · doi:10.1021/ac701744x · Cited by 18 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Podevin, M., Fotidis, I. A., & Angelidaki, I. (2018) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2017.1398132 · Cited by 34 · see citing works
- Authors
- Ren, L., Su, X., Wang, Y., Xu, J., & Ning, K.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Ning, Kang
- Venue
- PeerJ
- Publisher
- PeerJ
- Published
- 2014-06-26
- Volume
- 2
- Pages
- e436
- ISSN
- 2167-8359 (ISSN-L)
- PMC
- PMC4081187
- OpenAlex
- W2029022745
- Cited by
- 10
- References
- 25
- Citations by year
- 2026: 2 · 2023: 1 · 2019: 1 · 2017: 1 · 2016: 2 · 2015: 3
- FWCI
- 1.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- PeerJ (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Institutional knowledge base (Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences) (submittedVersion)
- Funders
- National Institute on Alcohol Abuse and Alcoholism (R34AA021502)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Engineering › Biomedical Engineering › Microfluidic and Bio-sensing Technologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- single-cell Raman spectroscopy, single-cell phenotyping, online process control, high-throughput data analysis, cellular heterogeneity, data mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
