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Cited by 135Spectroscopy Techniques in Biomedical and Chemical ResearchGold and Silver Nanoparticles Synthesis and ApplicationsQuantum Dots Synthesis And Properties

Spectral Analysis of Multiplex Raman Probe Signatures

Open access
Lutz, Barry R., Dentinger, Claire E., Nguyen, Lienchi N., Sun, Lei, Zhang, Jingwu, Allen, April N., Chan, Selena, Knudsen, Beatrice S.·2008·ACS Nano
DOI 10.1021/nn800243gPMC PMC2662378
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Abstract

Raman nanoparticle probes are an emerging new class of optical labels for interrogation of physiological and pathological processes in bioassays, cells, and tissues. Although their unique emission signatures are ideal for multiplexing, the full potential of these probes has not been realized because conventional analysis methods are inadequate. We report a novel spectral fitting method that exploits the entire spectral signature to quantitatively extract individual probe signals from multiplex spectra. We evaluate the method in a series of multiplex assays using unconjugated and antibody-conjugated composite organic−inorganic nanoparticles (COINs). Results show sensitive multiplex detection of small signals (<2% of total signal) and similar detection limits in corresponding 4-plex and singlet plate binding assays. In a triplex assay on formalin-fixed human prostate tissue, two antibody-conjugated COINs and a conventional fluorophore are used to image expression of prostate-specific antigen, cytokeratin-18, and DNA. The spectral analysis method effectively removes tissue autofluorescence and other unknown background, allowing accurate and reproducible imaging (area under ROC curve 0.89 ± 0.03) at subcellular spatial resolution. In all assay systems, the error attributable to spectral analysis constitutes ≤2% of total signal. The spectral fitting method provides (1) quantification of signals from multiplex spectra with overlapping peaks, (2) robust spot-by-spot removal of unknown background, (3) the opportunity to quantitatively assess the analysis error, (4) elimination of operator bias, and (5) simple automation appropriate for high-throughput analysis. The simple implementation and universal applicability of this approach significantly expands the potential of Raman probes for quantitative in vivo and ex vivo multiplex analysis.

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • SD does not require background subtraction, eliminating a major source of error, but is more sensitive to noise, leading to lower sensitivity.

    “This method does not require background subtraction, eliminating a major source of error but is more sensitive to noise, leading to lower sensitivity.”

    From Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics (2022) · cites this paper as [22]

    In Results and Discussion, under “Deconvolution of multiplex Raman spectra” (page unavailable), the paper says its fitting method does not require prior background subtraction and explains that subtracting a single background spectrum can miscalculate probe signals. Our copy does not support the added claim that the method is more sensitive to noise or consequently has lower sensitivity; it instead reports low-abundance signal detection. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Lutz et al. developed a spectral decomposition procedure that deconvolves multiplexed spectra by finding the best fit of normalized spectra to component reference spectra, with a free-fitting polynomial representing the background.

    “Lutz et al. developed a spectral decomposition (SD) procedure that deconvolves multiplexed spectra by finding the best fit of normalized spectra to component reference spectra, with a free‐fitting polynomial representing the background.[ 22 ]”

    From Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics (2022) · cites this paper as [22]

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Authors
Lutz, B. R., Dentinger, C. E., Nguyen, L. N., Sun, L., Zhang, J., Allen, A. N., Chan, S., & Knudsen, B. S.
Affiliations
  • Intel (United States) (US)
  • Fred Hutch Cancer Center (US)
Corresponding author
  • Chan, Selena
  • Knudsen, Beatrice S.
ORCID
Venue
ACS Nano
Publisher
American Chemical Society (ACS)
Published
2008-11-04
Volume
2
Issue
11
Pages
2306-2314
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
DOI
10.1021/nn800243g
PMC
PMC2662378
OpenAlex
W1972082742
Cited by
135
References
36
Citations by year
2026: 2 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 7 · 2021: 5 · 2020: 9 · 2019: 5 · 2018: 7 · 2017: 7 · 2016: 4 · 2015: 8 · 2014: 7 · 2013: 15 · 2012: 16
FWCI
5.24
Type
journal article
Language
en
Open access
Locations
  • ACS Nano (publishedVersion)
  • PubMed (publishedVersion)
  • authors@Fred Hutch (Fred Hutchinson Cancer Research Center) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Cancer Institute (P50-CA97186, P30CA015704, P50CA097186)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
  • Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Gold and Silver Nanoparticles Synthesis and Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Quantum Dots Synthesis And Properties
Keywords
spectral fitting, antibody-conjugated nanoparticles, prostate-specific antigen, cytokeratin 18, intracellular imaging, high-throughput analysis, optical labels
MeSH terms
Automation, Biological Assay, Fluorescent Dyes, Formaldehyde, Humans, Male, Paraffin, Prostatic Neoplasms, Regression Analysis, Spectrum Analysis, Raman, Prostate-Specific Antigen, Nanotechnology, Keratin-18, Nanoparticles, Optics and Photonics

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