Bessel-Beam Hyperspectral CARS Microscopy with Sparse Sampling: Enabling High-Content High-Throughput Label-Free Quantitative Chemical Imaging

Abstract

Microscopy-based high-content and high-throughput analysis of cellular systems plays a central role in drug discovery. However, for contrast and specificity, the majority of assays require a fluorescent readout which always comes with the risk of alteration of the true biological conditions. In this work, we demonstrate a label-free imaging platform which combines chemically specific hyperspectral coherent anti-Stokes Raman scattering microscopy with sparse sampling and Bessel beam illumination. This enabled us to screen multiwell plates at high speed, while retaining the high-content chemical analysis of hyperspectral imaging. To demonstrate the practical applicability of the method we addressed a critical side effect in drug screens, namely, drug-induced lipid storage within hepatic tissue. We screened 15 combinations of drugs and neutral lipids added to human HepG2 liver cells and developed a high-content quantitative data analysis pipeline which extracted the spectra and spatial distributions of lipid and protein components. We then used their combination to train a support vector machine discriminative algorithm. Classification of the drug responses in terms of phospholipidosis versus steatosis was achieved in a completely label-free assay.

Hyperspectral analysis applied to micro-Brillouin maps of amyloid-beta plaques in Alzheimer's disease brains

“…In this sense, even maps with larger point-to-point intervals can be exploited, hence speeding up the analysis. This has been shown using a combination of NMF and sparse sampling for CARS microscopy,26,27 and could be used also for Brillouin spectroscopy to allow for high speed mapping.…”

Review of high-content screening applications in toxicology

“…The majority of HCS assays require fluorescent readouts using GFP, potentially inadvertently altering native biological conditions. The first label-free (non-GFP) HCS assay targeting cellular lipid accumulation was developed and validated by combining Bessel beam illumination with sparse sampling acquisition in hyperspectral coherent anti-Stokes Raman scattering (CARS) imaging [116]. This label-free technology has an advantage over fluorescent labeling by not artificially disrupting the biology of the system, improving the biological relevance of HCS.…”

Mammalian cell and tissue imaging using Raman and coherent Raman microscopy

“…Least squares regression analysis is typically used for mapping samples of known compositions (Fu, Holtom, Freudiger, Zhang, & Xie, 2013; Lee, Moon, Migler, & Cicerone, 2011). For samples with little or no prior knowledge, methods such as principal component analysis (Lin et al, 2011), k‐means clustering (Krafft et al, 2009), spectral phasor analysis (Wei et al, 2019), Independent components analysis (Ozeki et al, 2012), multivariate curve resolution analysis (Zhang et al, 2013), as well as in‐house‐developed algorithm (Masia, Karuna, Borri, & Langbein, 2015; Masia, Pope, Watson, Langbein, & Borri, 2018) have been employed for mapping major components in Raman imaging data. Lately, machine learning (ML) algorithms have been adopted and developed to extract information from Raman spectra (mainly based on pre‐known knowledge), such as algorithms developed for assessing expressed human meibum (Alfonso‐Garcia et al, 2017), identification of pathogenic bacteria (Ho et al, 2019), and detecting prostate cancer (Lee, Lenferink, Otto, & Offerhaus, 2020).…”