Abstract:Challenging histopathological diagnostics in cancer include microsatellite instability-high (MSI-H) colorectal cancer (CRC), which occurs in 15% of early-stage CRC and is caused by a deficiency in the mismatch repair system. The diagnosis of MSI-H cannot be reliably achieved by visual inspection of a hematoxylin and eosin stained thin section alone, but additionally requires subsequent molecular analysis. Time-and sample-intensive immunohistochemistry with subsequent fragment length analysis is used. The aim o… Show more
“…The upper right cell turns circa 90° between 35 and 52 min, but in between its image appears fuzzy, with stripes along the direction of the scan that repeats every 3 s, indicating this cell changes position irregularly on a time scale of several s. Lastly, a third cell seen in the middle of the image at 35 min, detected for a few consecutive scans only, begins to look fuzzy in the 30 min image after it had been in a well-defined shape for the preceding half hour. In the future such dynamics could be tracked with orders-of-magnitude higher frame rate, by using optical-parametric or quantum-cascade laser sources 5 , 28 , 29 . Figure 5 Adhesion-localised, but mobile living E. coli cells, sequential s-SNOM infrared amplitude s 2 images as cells adhere, grow and move in the medium under a 15 nm SiN membrane (Norcada NBPX5002YZ-HR), at times (min) indicated, color scale as in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Pertaining spectra in the "fingerprint region" of wavelengths between 5 and 15 µm routinely determined by FTIR (Fourier-transform infrared) spectroscopy serve for quantitative chemical recognition in many fields of chemical analytics. This potential is also gaining increasing interest in the life sciences where, for example, advanced workflow procedures can classify embedded tumour tissue sections on the basis of minute spectral differences 5 .…”
Infrared fingerprint spectra can reveal the chemical nature of materials down to 20-nm detail, far below the diffraction limit, when probed by scattering-type scanning near-field optical microscopy (s-SNOM). But this was impossible with living cells or aqueous processes as in corrosion, due to water-related absorption and tip contamination. Here, we demonstrate infrared s-SNOM of water-suspended objects by probing them through a 10-nm thick SiN membrane. This separator stretches freely over up to 250 µm, providing an upper, stable surface to the scanning tip, while its lower surface is in contact with the liquid and localises adhering objects. We present its proof-of-principle applicability in biology by observing simply drop-casted, living E. coli in nutrient medium, as well as living A549 cancer cells, as they divide, move and develop rich sub-cellular morphology and adhesion patterns, at 150 nm resolution. Their infrared spectra reveal the local abundances of water, proteins, and lipids within a depth of ca. 100 nm below the SiN membrane, as we verify by analysing well-defined, suspended polymer spheres and through model calculations. SiN-membrane based s-SNOM thus establishes a novel tool of live cell nano-imaging that returns structure, dynamics and chemical composition. This method should benefit the nanoscale analysis of any aqueous system, from physics to medicine.
“…The upper right cell turns circa 90° between 35 and 52 min, but in between its image appears fuzzy, with stripes along the direction of the scan that repeats every 3 s, indicating this cell changes position irregularly on a time scale of several s. Lastly, a third cell seen in the middle of the image at 35 min, detected for a few consecutive scans only, begins to look fuzzy in the 30 min image after it had been in a well-defined shape for the preceding half hour. In the future such dynamics could be tracked with orders-of-magnitude higher frame rate, by using optical-parametric or quantum-cascade laser sources 5 , 28 , 29 . Figure 5 Adhesion-localised, but mobile living E. coli cells, sequential s-SNOM infrared amplitude s 2 images as cells adhere, grow and move in the medium under a 15 nm SiN membrane (Norcada NBPX5002YZ-HR), at times (min) indicated, color scale as in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Pertaining spectra in the "fingerprint region" of wavelengths between 5 and 15 µm routinely determined by FTIR (Fourier-transform infrared) spectroscopy serve for quantitative chemical recognition in many fields of chemical analytics. This potential is also gaining increasing interest in the life sciences where, for example, advanced workflow procedures can classify embedded tumour tissue sections on the basis of minute spectral differences 5 .…”
Infrared fingerprint spectra can reveal the chemical nature of materials down to 20-nm detail, far below the diffraction limit, when probed by scattering-type scanning near-field optical microscopy (s-SNOM). But this was impossible with living cells or aqueous processes as in corrosion, due to water-related absorption and tip contamination. Here, we demonstrate infrared s-SNOM of water-suspended objects by probing them through a 10-nm thick SiN membrane. This separator stretches freely over up to 250 µm, providing an upper, stable surface to the scanning tip, while its lower surface is in contact with the liquid and localises adhering objects. We present its proof-of-principle applicability in biology by observing simply drop-casted, living E. coli in nutrient medium, as well as living A549 cancer cells, as they divide, move and develop rich sub-cellular morphology and adhesion patterns, at 150 nm resolution. Their infrared spectra reveal the local abundances of water, proteins, and lipids within a depth of ca. 100 nm below the SiN membrane, as we verify by analysing well-defined, suspended polymer spheres and through model calculations. SiN-membrane based s-SNOM thus establishes a novel tool of live cell nano-imaging that returns structure, dynamics and chemical composition. This method should benefit the nanoscale analysis of any aqueous system, from physics to medicine.
“…The workflow with the RF classifier used for this work was established and described in previous publications. 29,35,36 The RF classifier was shown to be robust and reliable for tissue classification using IR imaging. 25,38e40 In this study, five consecutive RF classifiers were generated.…”
Section: Classifier Setup and Spectral Database Generationmentioning
confidence: 99%
“…In addition to the histochemical and immunohistochemical staining for subtyping lung cancer, the Institute of Pathology, University Hospital Cologne, sequenced an NGS gene panel to identify relevant mutations in lung tumor tissues. Previous studies showed that IR imaging can be used for biomarker identification, 26,28,36,44 otherwise performed by several IHC stainings. Therefore, to add a molecular dimension to the spatial IR resolution, herein, two additional RF classifiers were trained to identify mutations in lung cancer tissues (Figure 3).…”
Section: Analysis Of Mutations In Lung Adenocarcinomamentioning
confidence: 99%
“…The recognition of microsatellite stability and instability of cancerous tissue was verified with 100% sensitivity and 93% specificity compared with immunohistochemistry and fragment length analysis. 36 In this study, a label-free, automated, spatially resolved, and observer-/operator-independent approach using QCLbased IR imaging is presented. This technique was verified on thin sections of 536 formalin-fixed, paraffin-embedded (FFPE) tumor and nontumor lung tissues from 214 patients.…”
Label‐free and nondestructive mid‐infrared vibrational hyperspectral imaging is an essential tissue analysis tool, providing spatially resolved biochemical information critical to understanding physiological and pathological processes. However, the chemically complex and spatially heterogeneous composition of tissue specimens and the inherently weak interaction of infrared light with biomolecules limit the analytical performance of infrared absorption spectroscopy. Here, an advanced mid‐infrared spectrochemical tissue imaging modality is introduced using metasurfaces that support strong surface‐localized electromagnetic fields to capture quantitative molecular maps of large‐area murine brain tissue sections. The approach leverages polarization‐multiplexed multi‐resonance plasmonic metasurfaces to simultaneously detect various functional biomolecules. The surface‐enhanced mid‐infrared spectral imaging method eliminates the non‐specific effects of bulk tissue morphology on quantitative spectral analysis and improves chemical selectivity. This study shows that metasurface enhancement increases the retrieval of amide I and II bands associated with protein secondary structures. Moreover, it is demonstrated that plasmonic metasurfaces enhance the chemical contrast in infrared images and enable detection of ultrathin tissue regions that are not otherwise visible to conventional mid‐infrared spectral imaging. While this work uses murine brain tissue sections, the chemical imaging method is well‐suited for other tissue types, which broadens its potential impact for translational research and clinical histopathology.
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