Abstract
Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.
| Original language | English |
|---|---|
| Title of host publication | Biomedical Vibrational Spectroscopy VI |
| Subtitle of host publication | Advances in Research and Industry |
| Publisher | SPIE |
| ISBN (Print) | 9780819498526 |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | Biomedical Vibrational Spectroscopy VI: Advances in Research and Industry - San Francisco, CA, United States Duration: 2014 Feb 1 → 2014 Feb 2 |
Publication series
| Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
|---|---|
| Volume | 8939 |
| ISSN (Print) | 1605-7422 |
Conference
| Conference | Biomedical Vibrational Spectroscopy VI: Advances in Research and Industry |
|---|---|
| Country | United States |
| City | San Francisco, CA |
| Period | 14/2/1 → 14/2/2 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging
Cite this
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Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset. / Ashok, Praveen C.; Praveen, Bavishna B.; Campbell, Elaine C.; Dholakia, Kishan; Powis, Simon J.
Biomedical Vibrational Spectroscopy VI: Advances in Research and Industry. SPIE, 2014. 2039836 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 8939).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset
AU - Ashok, Praveen C.
AU - Praveen, Bavishna B.
AU - Campbell, Elaine C.
AU - Dholakia, Kishan
AU - Powis, Simon J.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.
AB - Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.
UR - http://www.scopus.com/inward/record.url?scp=84897458690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897458690&partnerID=8YFLogxK
U2 - 10.1117/12.2039836
DO - 10.1117/12.2039836
M3 - Conference contribution
AN - SCOPUS:84897458690
SN - 9780819498526
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Biomedical Vibrational Spectroscopy VI
PB - SPIE
ER -