Visualizing the appearance and disappearance of the attractor of differentiation using Raman spectral imaging

Ichimura, Taro; Chiu, Liang-da; Fujita, Katsumasa; Machiyama, Hiroaki; Kawata, Satoshi; Watanabe, Tomonobu M.; Fujita, Hideaki

doi:10.1038/srep11358

Cited by 22 publications

(22 citation statements)

References 30 publications

(38 reference statements)

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“…These characteristics are suitable for recognition of differentiation in regenerative medicine. To realize higher accuracy of the image recognition, another non/less-invasive imaging method could be combined, such as auto-fluorescent imaging [18], Raman imaging [19], imaging flowcytometer [20] and so on. Not only using single imaging technique, but the combination of some imaging technique and deep learning method will provide deeper information of single cells.…”

Section: Discussionmentioning

confidence: 99%

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

et al. 2017

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In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

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Section: Discussionmentioning

confidence: 99%

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

et al. 2017

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“…Alternative approaches of interpretation are to represent intrinsically high-dimensional cellular Raman spectra in low-dimensional spaces using dimension reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) (Goodacre et al, 1998;Huang et al, 2004;Jaumot et al, 2005;Ichimura et al, 2014Ichimura et al, , 2015. Though these methods can sometimes successfully assign the spectra from different cell types or states to distinct subspaces, the interpretation still remains unclear because the resulting axes and spaces usually cannot be characterized by any biological properties.…”

Section: Introductionmentioning

confidence: 99%

Linear Regression Links Transcriptomic Data and Cellular Raman Spectra

et al. 2018

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Raman microscopy is an imaging technique that has been applied to assess molecular compositions of living cells to characterize cell types and states. However, owing to the diverse molecular species in cells and challenges of assigning peaks to specific molecules, it has not been clear how to interpret cellular Raman spectra. Here, we provide firm evidence that cellular Raman spectra and transcriptomic profiles of Schizosaccharomyces pombe and Escherichia coli can be computationally connected and thus interpreted. We find that the dimensions of high-dimensional Raman spectra and transcriptomes measured by RNA sequencing can be reduced and connected linearly through a shared low-dimensional subspace. Accordingly, we were able to predict global gene expression profiles by applying the calculated transformation matrix to Raman spectra, and vice versa. Highly expressed non-coding RNAs contributed to the Raman-transcriptome linear correspondence more significantly than mRNAs in S. pombe. This demonstration of correspondence between cellular Raman spectra and transcriptomes is a promising step toward establishing spectroscopic live-cell omics studies.

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“…Recently, Raman spectroscopy and microscopy methods have attracted much attention because they provide alternative information based on the molecular vibrational signature. Spontaneous Raman scattering microscopy has been used to analyze epidermal components in vivo [16][17][18] and to visualize myoblasts during differentiation 19 and nucleic acids and tryptophan in human skin cells. 20 A more recent study reported the response of LCs to medical agents.…”

Section: Introductionmentioning

confidence: 99%

In situ visualization of intracellular morphology of epidermal cells using stimulated Raman scattering microscopy

et al. 2016

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Visualization of epidermal cells is important because the differentiation patterns of keratinocytes (KCs) are considered to be related to the functions and condition of skin. Optical microscopy has been widely used to investigate epidermal cells, but its applicability is still limited because of the need for sample fixation and staining. Here, we report our staining-free observation of epidermal cells in both tissue and culture by stimulated Raman scattering (SRS) microscopy that provides molecular vibrational contrast. SRS allowed us to observe a variety of cellular morphologies in skin tissue, including ladder-like structures in the spinous layer, enucleation of KCs in the granular layer, and three-dimensional cell column structures in the stratum corneum. We noticed that some cells in the spinous layer had a brighter signal in the cytoplasm than KCs. To examine the relevance of the observation of epidermal layers, we also observed cultured epidermal cells, including KCs at various differentiation stages, melanocytes, and Langerhans cell-like cells. Their SRS images also demonstrated various morphologies, suggesting that the morphological differences observed in tissue corresponded to the cell lineage. These results indicate the possible application of SRS microscopy to dermatological investigation of cell lineages and types in the epidermis by cellular-level analysis.

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Visualizing the appearance and disappearance of the attractor of differentiation using Raman spectral imaging

Cited by 22 publications

References 30 publications

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images

Linear Regression Links Transcriptomic Data and Cellular Raman Spectra

In situ visualization of intracellular morphology of epidermal cells using stimulated Raman scattering microscopy

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