scholarly journals Temporal Imaging of Live Cells by High-Speed Confocal Raman Microscopy

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3732
Author(s):  
Jeon Woong Kang ◽  
Freddy T. Nguyen ◽  
Niyom Lue
Keyword(s):  
High Speed ◽  
External Perturbation ◽  
Cell Types ◽  
Raman Microscopy ◽  
Live Cells ◽  
Confocal Raman Microscopy ◽  
Label Free ◽  
Minimal Sample ◽  
Confocal Raman

Label-free live cell imaging was performed using a custom-built high-speed confocal Raman microscopy system. For various cell types, cell-intrinsic Raman bands were monitored. The high-resolution temporal Raman images clearly delineated the intracellular distribution of biologically important molecules such as protein, lipid, and DNA. Furthermore, optical phase delay measured using quantitative phase microscopy shows similarity with the image reconstructed from the protein Raman peak. This reported work demonstrates that Raman imaging is a powerful label-free technique for studying various biomedical problems in vitro with minimal sample preparation and external perturbation to the cellular system.

scholarly journals Label-Free Live-Cell Imaging with Confocal Raman Microscopy

2012 ◽  
Vol 102 (2) ◽  
pp. 360-368 ◽  
Author(s):  
Katharina Klein ◽  
Alexander M. Gigler ◽  
Thomas Aschenbrenner ◽  
Roberto Monetti ◽  
Wolfram Bunk ◽  
...  
Keyword(s):  
Live Cell Imaging ◽  
Cell Imaging ◽  
Raman Microscopy ◽  
Live Cell ◽  
Confocal Raman Microscopy ◽  
Label Free ◽  
Confocal Raman

Label-free tracking of nanosized graphene oxide cellular uptake by confocal Raman microscopy

The Analyst ◽  
2018 ◽  
Vol 143 (15) ◽  
pp. 3686-3692 ◽  
Author(s):  
Marianna Eliášová Sohová ◽  
Michal Bodík ◽  
Peter Siffalovic ◽  
Nikola Bugárová ◽  
Martina Labudová ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Cellular Uptake ◽  
Cancer Diagnostics ◽  
Raman Microscopy ◽  
Two Dimensional ◽  
Confocal Raman Microscopy ◽  
Label Free ◽  
Confocal Raman ◽  
Diagnostics And Therapy

Graphene oxide (GO), a partially oxidized two-dimensional allotrope of carbon, is an attractive nanocarrier for cancer diagnostics and therapy.

scholarly journals Raman2RNA: Live-cell label-free prediction of single-cell RNA expression profiles by Raman microscopy

2021 ◽  
Author(s):  
Koseki J. Kobayashi-Kirschvink ◽  
Shreya Gaddam ◽  
Taylor James-Sorenson ◽  
Emanuelle Grody ◽  
Johain R. Ounadjela ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Vibrational Energy ◽  
Temporal Dynamics ◽  
Expression Profiles ◽  
Raman Microscopy ◽  
Live Cells ◽  
Label Free ◽  
Imaging Data

Single cell RNA-Seq (scRNA-seq) and other profiling assays have opened new windows into understanding the properties, regulation, dynamics, and function of cells at unprecedented resolution and scale. However, these assays are inherently destructive, precluding us from tracking the temporal dynamics of live cells, in cell culture or whole organisms. Raman microscopy offers a unique opportunity to comprehensively report on the vibrational energy levels of molecules in a label-free and non-destructive manner at a subcellular spatial resolution, but it lacks in genetic and molecular interpretability. Here, we developed Raman2RNA (R2R), an experimental and computational framework to infer single-cell expression profiles in live cells through label-free hyperspectral Raman microscopy images and multi-modal data integration and domain translation. We used spatially resolved single-molecule RNA-FISH (smFISH) data as anchors to link scRNA-seq profiles to the paired spatial hyperspectral Raman images, and trained machine learning models to infer expression profiles from Raman spectra at the single-cell level. In reprogramming of mouse fibroblasts into induced pluripotent stem cells (iPSCs), R2R accurately (r>0.96) inferred from Raman images the expression profiles of various cell states and fates, including iPSCs, mesenchymal-epithelial transition (MET) cells, stromal cells, epithelial cells, and fibroblasts. R2R outperformed inference from brightfield images, showing the importance of spectroscopic content afforded by Raman microscopy. Raman2RNA lays a foundation for future investigations into exploring single-cell genome-wide molecular dynamics through imaging data, in vitro and in vivo.

scholarly journals Label-free detection of anticancer drug paclitaxel in living cells by confocal Raman microscopy

2013 ◽  
Vol 102 (11) ◽  
pp. 113701 ◽  
Author(s):  
H. Salehi ◽  
L. Derely ◽  
A.-G. Vegh ◽  
J.-C. Durand ◽  
C. Gergely ◽  
...  
Keyword(s):  
Anticancer Drug ◽  
Living Cells ◽  
Raman Microscopy ◽  
Confocal Raman Microscopy ◽  
Label Free ◽  
Label Free Detection ◽  
Confocal Raman

scholarly journals Label-free live cell imaging by Confocal Raman Microscopy identifies CHO host and producer cell lines

2016 ◽  
Vol 12 (1) ◽  
pp. 1600037 ◽  
Author(s):  
Batirtze Prats Mateu ◽  
Eva Harreither ◽  
Markus Schosserer ◽  
Verena Puxbaum ◽  
Elisabeth Gludovacz ◽  
...  
Keyword(s):  
Cell Lines ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Raman Microscopy ◽  
Live Cell ◽  
Confocal Raman Microscopy ◽  
Label Free ◽  
Producer Cell ◽  
Confocal Raman

scholarly journals Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy

2021 ◽  
Vol 12 ◽  
Author(s):  
Lukas Simon Kriem ◽  
Kevin Wright ◽  
Renzo Alberto Ccahuana-Vasquez ◽  
Steffen Rupp
Keyword(s):  
Laser Scanning ◽  
Bacterial Species ◽  
Raman Microscopy ◽  
Laser Scanning Microscopy ◽  
Confocal Laser ◽  
Spectroscopic Techniques ◽  
Confocal Raman Microscopy ◽  
Biofilm Model ◽  
Confocal Raman

Techniques for continuously monitoring the formation of subgingival biofilm, in relation to the determination of species and their accumulation over time in gingivitis and periodontitis, are limited. In recent years, advancements in the field of optical spectroscopic techniques have provided an alternative for analyzing three-dimensional microbiological structures, replacing the traditional destructive or biofilm staining techniques. In this work, we have demonstrated that the use of confocal Raman spectroscopy coupled with multivariate analysis provides an approach to spatially differentiate bacteria in an in vitro model simulating a subgingival dual-species biofilm. The present study establishes a workflow to evaluate and differentiate bacterial species in a dual-species in vitro biofilm model, using confocal Raman microscopy (CRM). Biofilm models of Actinomyces denticolens and Streptococcus oralis were cultured using the “Zürich in vitro model” and were analyzed using CRM. Cluster analysis was used to spatially differentiate and map the biofilm model over a specified area. To confirm the clustering of species in the cultured biofilm, confocal laser scanning microscopy (CLSM) was coupled with fluorescent in vitro hybridization (FISH). Additionally, dense bacteria interface area (DBIA) samples, as an imitation of the clusters in a biofilm, were used to test the developed multivariate differentiation model. This confirmed model was successfully used to differentiate species in a dual-species biofilm and is comparable to morphology. The results show that the developed workflow was able to identify main clusters of bacteria based on spectral “fingerprint region” information from CRM. Using this workflow, we have demonstrated that CRM can spatially analyze two-species in vitro biofilms, therefore providing an alternative technique to map oral multi-species biofilm models.

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