Non-label immune cell state prediction using Raman spectroscopy

Abstract

The acquired immune system, mainly composed of T and B lymphocytes, plays a key role in protecting the host from infection. It is important and technically challenging to identify cell types and their activation status in living and intact immune cells, without staining or killing the cells. Using Raman spectroscopy, we succeeded in discriminating between living T cells and B cells, and visualized the activation status of living T cells without labeling. Although the Raman spectra of T cells and B cells were similar, they could be distinguished by discriminant analysis of the principal components. Raman spectra of activated T cells with anti-CD3 and anti-CD28 antibodies largely differed compared to that of naïve T cells, enabling the prediction of T cell activation status at a single cell level. Our analysis revealed that the spectra of individual T cells gradually change from the pattern of naïve T cells to that of activated T cells during the first 24 h of activation, indicating that changes in Raman spectra reflect slow changes rather than rapid changes in cell state during activation. Our results indicate that the Raman spectrum enables the detection of dynamic changes in individual cell state scattered in a heterogeneous population.

Raman Spectroscopy Follows Time-Dependent Changes in T Lymphocytes Isolated from Spleen of Endotoxemic Mice

“…Further, to sustain the large changes in the nucleus, large numbers of proteins are required in the activated T cells compared with the naive cells. As a result, immune-specific transcription factors upregulating the T cells' surface glycoproteins (17,55,56) show major contributions in the Raman difference spectra apart from Raman peaks of nucleic acids (57).…”

“…Raman spectra of the tissue have contributions from various splenocytes and can influence the outcome of the PCA-LDA model. It has been previously reported that Raman spectroscopy is capable of differentiating between T cells and B cells (57). Because the tissue areas for Raman mapping were selected randomly, the possibility of uneven distribution of the T cells and B cells within the Raman-mapped area can be foreseen.…”

Landscape of Manufacturing Process of ATMP Cell Therapy Products for Unmet Clinical Needs

“…That is true for standardized and reproducible expansion of defined immune cell preparations as well as for estimates and methods of measuring specific functionalities of an expanded immune cell population against tumor cells, infections, or inflammations. Moreover, there are also missing generally available methods and techniques for fast and precise measurement of homogeneity of a cell population, of characteristics of sub populations, and single cells [10][11][12][13]. All these aspects appear to be widely responsible for the limited progress by these very promising new therapies.…”

“…However, the combination of this method with sophisticated software programs with in-depth analyses tools can lead to sharper, high-resolution Raman spectra enabling differentiating looks onto cells enabling subtype identification, quantification, analysis of functional status, etc. [10][11][12][13].…”

A Proposal for Automated Background Removal of Bio-Raman Data

“…[6][7][8][9][10][11][12][13] Multivariable analysis, such as principal component analysis (PCA) or a PCA-based approach, called chemometrics in chemistry, is frequently applied to bio-Raman research for visualizing and extracting intrinsic information. [12][13][14][15][16][17][18][19][20][21] This is because bio-Raman data are generally complicated and large, making it almost impossible to interpret the data in an intuitive manner. For example, the number of spectra can easily reach a thousand when the time lapse of cellular differentiation is monitored, which typically takes several days.…”