Significance

Follicular thyroid carcinoma carries a substantially poor prognosis due to its unique biological behavior and less favorable outcomes. In particular, fine-needle aspiration (FNA) biopsies, which play a key role in screening thyroid nodules, cannot differentiate benign from malignant follicular neoplasm.

Aim

We report on the use of hyperspectral Raman microscopy in combination with chemometric analysis for identifying and classifying single cells obtained from clinical samples of human follicular thyroid neoplasms.

Approach

We used a method intended to simulate the FNA procedure to obtain single cells from thyroid nodules. A total of 392 hyperspectral Raman images of single cells from follicular thyroid neoplasms were collected.

Results

Malignant cells were identified based on their intrinsic Raman spectral signatures with an overall diagnostic accuracy of up to 83.7%.

Conclusions

Our findings indicate that hyperspectral Raman microscopy can potentially be developed into an ancillary test for analyzing single cells from thyroid FNA biopsies to better stratify "indeterminate" nodules and other cytologically challenging cases.

Human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes have many promising applications, including the regeneration of injured heart muscles, cardiovascular disease modeling, and drug cardiotoxicity screening. Current differentiation protocols yield a heterogeneous cell population that includes pluripotent stem cells and different cardiac subtypes (pacemaking and contractile cells). The ability to purify these cells and obtain well-defined, controlled cell compositions is important for many downstream applications; however, there is currently no established and reliable method to identify hiPSC-derived cardiomyocytes and their subtypes. Here, we demonstrate that second harmonic generation (SHG) signals generated directly from the myosin rod bundles can be a label-free, intrinsic optical marker for identifying hiPSC-derived cardiomyocytes. A direct correlation between SHG signal intensity and cardiac subtype is observed, with pacemaker-like cells typically exhibiting ~70% less signal strength than atrial- and ventricular-like cardiomyocytes. These findings suggest that pacemaker-like cells can be separated from the heterogeneous population by choosing an SHG intensity threshold criteria. This work lays the foundation for developing an SHG-based high-throughput flow sorter for purifying hiPSC-derived cardiomyocytes and their subtypes.

Modern flow cytometry instruments have become vital tools for high-throughput analysis of single cells. However, as issues with the cellular labeling techniques often used in flow cytometry have become more of a concern, the development of label-free modalities for cellular analysis is increasingly desired. Non-linear optical phenomena (NLO) are of growing interest for label-free analysis because of the ability to measure the intrinsic optical response of biomolecules found in cells. We demonstrate that a light-sheet consisting of a scanned Bessel beam is an optimal excitation geometry for efficiently generating NLO signals in a microfluidic environment. The balance of photon density and cross-sectional area provided by the light-sheet allowed significantly larger two-photon fluorescence intensities to be measured in a model polystyrene microparticle system compared to measurements made using other excitation focal geometries, including a relaxed Gaussian excitation beam often used in conventional flow cytometers.

Laser tweezers Raman spectroscopy (LTRS) was used to acquire the Raman spectra of leukemic T lymphocytes exposed to the chemotherapy drug doxorubicin at different time points over 72 hours. Changes observed in the Raman spectra were dependent on drug exposure time and concentration. The sequence of spectral changes includes an intensity increase in lipid Raman peaks, followed by an intensity increase in DNA Raman peaks, and finally changes in DNA and protein (phenylalanine) Raman vibrations. These Raman signatures are consistent with vesicle formation, cell membrane blebbing, chromatin condensation, and the cytoplasm of dead cells during the different stages of drug-induced apoptosis. These results suggest the potential of LTRS as a real-time single cell tool for monitoring apoptosis, evaluating the efficacy of chemotherapeutic treatments, or pharmaceutical testing.

Medullary thyroid carcinoma (MTC) is a rare form of thyroid malignancy that can be diagnostically challenging on fine needle aspiration (FNA) cytology. Ancillary tests such as elevated serum or immunohistochemical positive calcitonin have been helpful, yet they can occasionally provide false positive results. In search for an alternative method to improve diagnostic accuracy (DA), we applied hyperspectral Raman spectroscopy to characterize the biochemical composition of single cells from MTC and compared their spectral information to cells from other types of thyroid nodules. Hyperspectral Raman images of 117 MTC single cells from digested tissue were obtained with a line-scan hyperspectral Raman microscope and compared to 127 benign and 121 classic variant of papillary thyroid carcinoma (CVPTC) cells. When principal component analysis and linear discriminant analysis were used to classify the spectral data, MTC cells were differentiated from benign and CVPTC cells with 97% and 99% DA, respectively. In addition, MTC cells exhibited a prominent Raman peak at 1003 cm^-1, whose intensity is 84% and 226% greater on average than that observed in benign and CVPTC cells, respectively. When specifically utilizing only this peak as a spectral marker, MTC cells were separated from benign and CVPTC cells with 87% and 95% DA, respectively. As this peak is linked to phenylalanine, which is known to be associated with calcitonin release in thyroid parafollicular cells, the increased intensity further suggests that this Raman peak could potentially be a new diagnostic marker for MTC. Furthermore, preliminary data from MTC cells (n=21) isolated from a simulated FNA procedure provided similar Raman signatures when compared to single cells from digestion. These results suggest that "Raman-based cytopathology" can be used as an adjunct technique to improve the diagnostic accuracy of FNA cytopathology at a single cell level.

We report on the use of line-scan hyperspectral Raman microscopy in combination with multivariate statistical analyses for identifying and classifying single cells isolated from clinical samples of human thyroid nodules based on their intrinsic Raman spectral signatures. A total of 248 hyperspectral Raman images of single cells from benign thyroid (n = 127) and classic variant of papillary carcinoma (n = 121) nodules were collected. Spectral differences attributed to phenylalanine, tryptophan, proteins, lipids, and nucleic acids were identified for benign and papillary carcinoma cells. Using principal component analysis and linear discriminant analysis, cells were identified with 97% diagnostic accuracy. In addition, preliminary data of cells from follicular adenoma (n = 20), follicular carcinoma (n = 25), and follicular variant of papillary carcinoma (n = 18) nodules suggest the feasibility of further discrimination of subtypes. Our findings indicate that hyperspectral Raman microscopy can potentially be developed into an objective approach for analyzing single cells from fine needle aspiration (FNA) biopsies to enable the minimally invasive diagnosis of "indeterminate" thyroid nodules and other challenging cases.

Insights into the expression of pacemaker-specific markers in human induced pluripotent stem cell (hiPSC)-derived cardiomyocyte subtypes can facilitate the enrichment and track differentiation and maturation of hiPSC-derived pacemaker-like cardiomyocytes. To date, no study has directly assessed gene expression in each pacemaker-, atria-, and ventricular-like cardiomyocyte subtype derived from hiPSCs since currently the subtypes of these immature cardiomyocytes can only be identified by action potential profiles. Traditional acquisition of action potentials using patch-clamp recordings renders the cells unviable for subsequent analysis. We circumvented these issues by acquiring the action potential profile of a single cell optically followed by assessment of protein expression through immunostaining in that same cell. Our same-single-cell analysis for the first time revealed expression of proposed pacemaker-specific markers-hyperpolarization-activated cyclic nucleotide-modulated (HCN)4 channel and Islet (Isl)1-at the protein level in all three hiPSC-derived cardiomyocyte subtypes. HCN4 expression was found to be higher in pacemaker-like hiPSC-derived cardiomyocytes than atrial- and ventricular-like subtypes but its downregulation over time in all subtypes diminished the differences. Isl1 expression in pacemaker-like hiPSC-derived cardiomyocytes was initially not statistically different than the contractile subtypes but did become statistically higher than ventricular-like cells with time. Our observations suggest that although HCN4 and Isl1 are differentially expressed in hiPSC-derived pacemaker-like relative to ventricular-like cardiomyocytes, these markers alone are insufficient in identifying hiPSC-derived pacemaker-like cardiomyocytes. Stem Cells 2016;34:2670-2680.

Imaging the structure and correlating it with the biochemical content of the retina holds promise for fundamental research and for clinical applications. Optical coherence tomography (OCT) is commonly used to image the 3D structure of the retina and while the added functionality of biochemical analysis afforded by Raman scattering could provide critical molecular signatures for clinicians and researchers, there are many technical challenges to combine these imaging modalities. We describe an OCT microscope for ex-vivo imaging combined with Raman spectroscopy capable of collecting morphological and molecular information about a sample simultaneously. We present our first results and discuss the challenges to further development of this dual-mode instrument and limitations for future in-vivo retinal imaging.

Surface enhanced Raman scattering (SERS) nanoparticles are an attractive alternative to fluorescent probes for biological labeling because of their photostability and multiplexing capabilities. However, nanoparticle size, shape, and surface properties are known to affect nanoparticle-cell interactions. Other issues such as the formation of a protein corona and antibody multivalency interfere with the labeling properties of nanoparticle-antibody conjugates. Hence, it is important to consider these aspects in order to validate such conjugates for live cell imaging applications. Using SERS nanoparticles that target HER2 and CD44 in breast cancer cells, we demonstrate labeling of fixed cells with high specificity that correlates well with fluorescent labels. However, when labeling live cells to monitor surface biomarker expression and dynamics, the nanoparticles are rapidly uptaken by the cells and become compartmentalized into different cellular regions. This behavior is in stark contrast to that of fluorescent antibody conjugates. This study highlights the impact of nanoparticle internalization and trafficking on the ability to use SERS nanoparticle-antibody conjugates to monitor cell dynamics.

Hirschsprung disease (HD) is a congenital disorder in the distal colon that is characterized by the absence of nerve ganglion cells in the diseased tissue. The primary treatment for HD is surgical intervention with resection of the aganglionic bowel. The accurate identification of the aganglionic segment depends on the histologic evaluation of multiple biopsies to determine the absence of ganglion cells in the tissue, which can be a time-consuming procedure. We investigate the feasibility of using a combination of label-free optical modalities, second harmonic generation (SHG); two-photon excitation autofluorescence (2PAF); and Raman spectroscopy (RS), to accurately locate and identify ganglion cells in murine intestinal tissue without the use of exogenous labels or dyes. We show that the image contrast provided by SHG and 2PAF signals allows for the visualization of the overall tissue morphology and localization of regions that may contain ganglion cells, while RS provides detailed multiplexed molecular information that can be used to accurately identify specific ganglion cells. Support vector machine, principal component analysis and linear discriminant analysis classification models were applied to the hyperspectral Raman data and showed that ganglion cells can be identified with a classification accuracy higher than 95%. Our findings suggest that a near real-time intraoperative histology method can be developed using these three optical modalities together that can aid pathologists and surgeons in rapid, accurate identification of ganglion cells to guide surgical decisions with minimal human intervention.