Towards quantitative molecular mapping of cells by Raman microscopy: Using AFM for decoupling molecular concentration and cell topography

Raman micro-spectroscopy (RMS) is a non-invasive technique for imaging live cells in vitro. However, obtaining quantitative molecular information from Raman spectra is difficult because the intensity of a Raman band is proportional to the number of molecules in the sampled volume, which depends on the local molecular concentration and the thickness of the cell. In order to understand these effects, we combined RMS with atomic force microscopy (AFM), a technique that can measure accurately the thickness profile of the cells. Solution-based calibration models for RNA and albumin were developed to create quantitative maps of RNA and proteins in individual fixed cells. The maps were built by applying the solution-based calibration models, based on partial least squares fitting (PLS), on raster-scan Raman maps, after accounting for the local cell height obtained from the AFM. We found that concentrations of RNA in the cytoplasm of mouse neuroprogenitor stem cells (NSCs) were as high as 25 ± 6 mg ml-1, while proteins were distributed more uniformly and reached concentrations as high as ∼50 ± 12 mg ml-1. The combined AFM-Raman datasets from fixed cells were also used to investigate potential improvements for normalization of Raman spectral maps. For all Raman maps of fixed cells (n = 10), we found a linear relationship between the scores corresponding to the first component (PC1) and the cell height profile obtained by AFM. We used PC1 scores to reconstruct the relative height profiles of independent cells (n = 10), and obtained correlation coefficients with AFM maps higher than 0.99. Using this normalization method, qualitative maps of RNA and protein were used to obtain concentrations for live NSCs. While this study demonstrates the potential of using AFM and RMS for measuring concentration maps for individual NSCs in vitro, further studies are required to establish the robustness of the normalization method based on principal component analysis when comparing Raman spectra of cells with large morphological differences

Intra-operative spectroscopic assessment of surgical margins during breast conserving surgery

Background: In over 20% of breast conserving operations, postoperative pathological assessment of the excised tissue reveals positive margins, requiring additional surgery. Current techniques for intra-operative assessment of tumor margins are insufficient in accuracy or resolution to reliably detect small tumors. There is a distinct need for a fast technique to accurately identify tumors smaller than 1 mm2 in large tissue surfaces within 30 min. Methods: Multi-modal spectral histopathology (MSH), a multimodal imaging technique combining tissue auto-fluorescence and Raman spectroscopy was used to detect microscopic residual tumor at the surface of the excised breast tissue. New algorithms were developed to optimally utilize auto-fluorescence images to guide Raman measurements and achieve the required detection accuracy over large tissue surfaces (up to 4 × 6.5 cm2). Algorithms were trained on 91 breast tissue samples from 65 patients. Results: Independent tests on 121 samples from 107 patients - including 51 fresh, whole excision specimens - detected breast carcinoma on the tissue surface with 95% sensitivity and 82% specificity. One surface of each uncut excision specimen was measured in 12–24 min. The combination of high spatial-resolution auto-fluorescence with specific diagnosis by Raman spectroscopy allows reliable detection even for invasive carcinoma or ductal carcinoma in situ smaller than 1 mm2. Conclusions: This study provides evidence that this multimodal approach could provide an objective tool for intra-operative assessment of breast conserving surgery margins, reducing the risk for unnecessary second operations