Search

Microcalcifications (MC) are observed in various tissues and in relation to several diseases. For breast cancer, recent studies have reported differences in the nature of the MC and correlations to the degree of malignancy of the neoplasm. Here, investigations of benign, ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) breast MC using X-ray fluorescence, X-ray absorption spectroscopy and wide-angle X-ray scattering are reported. While Mg has been observed in all MC, only for the benign MC has a rim of crystalline whitlockite been identified as a minor crystalline phase in addition to the major hy-droxy-apatite (HAP) one. MC in DCIS and IDC tissue exhibit a higher abundance of a high-crystallinity HAP phase in comparison with the less well ordered MC in the benign tissue. Moreover, the distribution of other trace elements in the MC, such as Na, S, Cl, Sr and Y, is observed. For the quantitative analysis of the elemental maps, the experimentally determined sample thickness in each pixel has been incorporated as an additional parameter in the fitting process to account for sample roughness., (© Thomas Huthwelker et al. 2025.)

Broadband Coherent anti-Stokes Raman (BCARS) microscopy is an imaging technique that can acquire full Raman spectra (400-3200 cm -1 ) of biological samples within a few milliseconds. However, the CARS signal suffers from an undesired non-resonant background (NRB), deriving from four-wave-mixing processes, which distorts the peak line shapes and reduces the chemical contrast. Traditionally, the NRB is removed using numerical algorithms that require expert users and knowledge of the NRB spectral profile. Recently, deep-learning models proved to be powerful tools for unsupervised automation and acceleration of NRB removal. Here, we thoroughly review the existing NRB removal deep-learning models (SpecNet, VECTOR, LSTM, Bi-LSTM) and present two novel architectures. The first one combines convolutional layers with Gated Recurrent Units (CNN + GRU); the second one is a Generative Adversarial Network (GAN) that trains an encoder-decoder network and an adversarial convolutional neural network. We also introduce an improved training dataset, generalized on different BCARS experimental configurations. We compare the performances of all these networks on test and experimental data, using them in the pipeline for spectral unmixing of BCARS images. Our analyses show that CNN + GRU and VECTOR are the networks giving the highest accuracy, GAN is the one that predicts the highest number of true positive peaks in experimental data, whereas GAN and VECTOR are the most suitable ones for real-time processing of BCARS images., (© 2024. The Author(s).)

Graphene-based materials (GBMs) are of considerable interest for biomedical applications, and the pilot study on the toxicological and immunological impact of pristine graphene (GR) and graphene oxide (GO) using swine as a close-to-human provides valuable insights. First, ex vivo experiments are conducted on swine blood cells, then GBMs are injected intraperitoneally (i.p.) into swine. Hematological and biochemical analyses at various intervals indicate that neither GO nor GR cause systemic inflammation, pro-coagulant responses, or renal or hepatic dysfunction. Importantly, no systemic toxicity is observed. Analysis of a panel of 84 immune-related genes shows minimal impact of GO and GR. The animals are sacrificed 21 days post-injection, and transient absorption imaging and Raman mapping show the presence of GO and GR in the mesentery only. Histological evaluation reveals no signs of alterations in other organs. Thus, clusters of both materials are detected in the mesentery, and GO aggregates are surrounded only by macrophages with the formation of granulomas. In contrast, modest local reactions are observed around the GR clusters. Overall, these results reveal that i.p. injection of GBMs resulted in a modest local tissue reaction without systemic toxicity. This study, performed in swine, provides essential guidance for future biomedical applications of graphene., (© 2024 The Author(s). Advanced Healthcare Materials published by Wiley‐VCH GmbH.)

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research. The BCARS data were acquired from two different experimental setups (Leibniz Institute of Photonics Technology (IPHT) in Jena and Politecnico di Milano (POLIMI) in Milano) to evaluate the reproducibility of results. Also, spontaneous Raman data were independently acquired at POLIMI to validate those results. First, Kramers-Kronig (KK) algorithm was utilized to retrieve Raman-like signals from the BCARS data, and a pre-processing pipeline was subsequently used to standardize the data. Principal component analysis - Linear discriminant analysis (PCA-LDA) was performed using two cross-validation (CV) methods: batch-out CV and 10-fold CV. Additionally, the analysis was repeated, considering different spectral regions of the data as input to the PCA-LDA. Finally, the classification accuracies of the two BCARS datasets were compared with the results of spontaneous Raman data. The results demonstrated that the CH band region (2770-3070 cm -1 ) and spectral data in the 1500-1800 cm -1 region have significantly contributed to the classification. A maximum of 100% balanced accuracies were obtained for the 10-fold CV for both BCARS setups. However, in the case of batch-out CV, it is 92.4% for the IPHT dataset and 98.8% for the POLIMI dataset. This study offers a comprehensive overview for estimating biological variance in biomedical applications. The insights gained from this analysis hold promise for improving the reliability of BCARS measurements in biomedical applications, paving the way for more accurate and meaningful spectroscopic analyses in the study of biological systems.

Accurate, rapid and non-invasive cancer cell phenotyping is a pressing concern across the life sciences, as standard immuno-chemical imaging and omics require extended sample manipulation. Here we combine Raman micro-spectroscopy and phase tomography to achieve label-free morpho-molecular profiling of human colon cancer cells, following the adenoma, carcinoma, and metastasis disease progression, in living and unperturbed conditions. We describe how to decode and interpret quantitative chemical and co-registered morphological cell traits from Raman fingerprint spectra and refractive index tomograms. Our multimodal imaging strategy rapidly distinguishes cancer phenotypes, limiting observations to a low number of pristine cells in culture. This synergistic dataset allows us to study independent or correlated information in spectral and tomographic maps, and how it benefits cell type inference. This method is a valuable asset in biomedical research, particularly when biological material is in short supply, and it holds the potential for non-invasive monitoring of cancer progression in living organisms., (© 2024. The Author(s).)

Acoustic vibrations of matter convey fundamental viscoelastic information that can be optically retrieved by hyperfine spectral analysis of the inelastic Brillouin scattered light. Increasing evidence of the central role of the viscoelastic properties in biological processes has stimulated the rise of non-contact Brillouin microscopy, yet this method faces challenges in turbid samples due to overwhelming elastic background light. Here, we introduce a common-path Birefringence-Induced Phase Delay (BIPD) filter to disentangle the polarization states of the Brillouin and Rayleigh signals, enabling the rejection of the background light using a polarizer. We demonstrate a 65 dB extinction ratio in a single optical pass collecting Brillouin spectra in extremely scattering environments and across highly reflective interfaces. We further employ the BIPD filter to image bone tissues from a mouse model of osteopetrosis, highlighting altered biomechanical properties compared to the healthy control. Results herald new opportunities in mechanobiology where turbid biological samples remain poorly characterized., (© 2024. The Author(s).)

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.

Diffuse Raman spectroscopy (DIRS) extends the high chemical specificity of Raman scattering to in-depth investigation of thick biological tissues. We present here a novel approach for time-domain diffuse Raman spectroscopy (TD-DIRS) based on a single-pixel detector and a digital micromirror device (DMD) within an imaging spectrometer for wavelength encoding. This overcomes the intrinsic complexity and high cost of detection arrays with ps-resolving time capability. Unlike spatially offset Raman spectroscopy (SORS) or frequency offset Raman spectroscopy (FORS), TD-DIRS exploits the time-of-flight distribution of photons to probe the depth of the Raman signal at a single wavelength with a single source-detector separation. We validated the system using a bilayer tissue-bone mimicking phantom composed of a 1 cm thick slab of silicone overlaying a calcium carbonate specimen and demonstrated a high differentiation of the two Raman signals. We reconstructed the Raman spectra of the two layers, offering the potential for improved and quantitative material analysis. Using a bilayer phantom made of porcine muscle and calcium carbonate, we proved that our system can retrieve Raman peaks even in the presence of autofluorescence typical of biomedical tissues. Overall, our novel TD-DIRS setup proposes a cost-effective and high-performance approach for in-depth Raman spectroscopy in diffusive media., Competing Interests: The authors declare no potential conflicts of interest., (© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.)

Anticancer therapy screening in vitro identifies additional treatments and improves clinical outcomes. Systematically, although most tested cells respond to cues with apoptosis, an appreciable portion enters a senescent state, a critical condition potentially driving tumor resistance and relapse. Conventional screening protocols would strongly benefit from prompt identification and monitoring of therapy-induced senescent (TIS) cells in their native form. We combined complementary all-optical, label-free, and quantitative microscopy techniques, based on coherent Raman scattering, multiphoton absorption, and interferometry, to explore the early onset and progression of this phenotype, which has been understudied in unperturbed conditions. We identified TIS manifestations as early as 24 hours following treatment, consisting of substantial mitochondrial rearrangement and increase of volume and dry mass, followed by accumulation of lipid vesicles starting at 72 hours. This work holds the potential to affect anticancer treatment research, by offering a label-free, rapid, and accurate method to identify initial TIS in tumor cells.

Identification of reliable and accessible biomarkers to characterize ischemic stroke patients' prognosis remains a clinical challenge. Neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are markers of brain injury, detectable in blood by high-sensitive technologies. Our aim was to measure serum NfL and GFAP after stroke, and to evaluate their correlation with functional outcome and the scores in rehabilitation scales at 3-month follow-up. Stroke patients were prospectively enrolled in a longitudinal observational study within 24 hours from symptom onset (D1) and monitored after 7 (D7), 30 ± 3 (M1) and 90 ± 5 (M3) days. At each time-point serum NfL and GFAP levels were measured by Single Molecule Array and correlated with National Institute of Health Stroke Scale (NIHSS), modified Rankin scale (mRS), Trunk Control Test (TCT), Functional Ambulation Classification (FAC) and Functional Independence Measure (FIM) scores. Serum NfL and GFAP showed different temporal profiles: NfL increased after stroke with a peak value at D7; GFAP showed an earlier peak at D1. NfL and GFAP concentrations correlated with clinical/rehabilitation outcomes both longitudinally and prospectively. Multivariate analysis revealed that NfL-D7 and GFAP-D1 were independent predictors of 3-month NIHSS, TCT, FAC and FIM scores, with NfL being the biomarker with the best predictive performance.

The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sorrentino, Manetti, Bresci, Vernuccio, Ceconello, Ghislanzoni, Bongarzone, Vanna, Cerullo and Polli.)

Coherent anti-Stokes Raman scattering (CARS) microscopy is an emerging nonlinear vibrational imaging technique that delivers label-free chemical maps of cells and tissues. In narrowband CARS, two spatiotemporally superimposed picosecond pulses, pump and Stokes, illuminate the sample to interrogate a single vibrational mode. Broadband CARS (BCARS) combines narrowband pump pulses with broadband Stokes pulses to record broad vibrational spectra. Despite recent technological advancements, BCARS microscopes still struggle to image biological samples over the entire Raman-active region (400-3100 cm -1 ). Here, we demonstrate a robust BCARS platform that answers this need. Our system is based on a femtosecond ytterbium laser at a 1035 nm wavelength and a 2 MHz repetition rate, which delivers high-energy pulses used to produce broadband Stokes pulses by white-light continuum generation in a bulk YAG crystal. Combining such pulses, pre-compressed to sub-20 fs duration, with narrowband pump pulses, we generate a CARS signal with a high (<9 cm -1 ) spectral resolution in the whole Raman-active window, exploiting both the two-color and three-color excitation mechanisms. Aided by an innovative post-processing pipeline, our microscope allows us to perform high-speed (≈1 ms pixel dwell time) imaging over a large field of view, identifying the main chemical compounds in cancer cells and discriminating tumorous from healthy regions in liver slices of mouse models, paving the way for applications in histopathological settings.

Bone tissue features a complex microarchitecture and biomolecular composition, which determine biomechanical properties. In addition to state-of-the-art technologies, innovative optical approaches allowing the characterization of the bone in native, label-free conditions can provide new, multi-level insight into this inherently challenging tissue. Here, we exploited multimodal nonlinear optical (NLO) microscopy, including co-registered stimulated Raman scattering, two-photon excited fluorescence, and second-harmonic generation, to image entire vertebrae of murine spine sections. The quantitative nature of these nonlinear interactions allowed us to extract accurate biochemical, morphological, and topological information on the bone tissue and to highlight differences between normal and pathologic samples. Indeed, in a murine model showing bone loss, we observed increased collagen and lipid content as compared to the wild type, along with a decreased craniocaudal alignment of bone collagen fibres. We propose that NLO microscopy can be implemented in standard histopathological analysis of bone in preclinical studies, with the ambitious future perspective to introduce this technique in the clinical practice for the analysis of larger tissue sections., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Talone, Bresci, Manetti, Vernuccio, De la Cadena, Ceconello, Schiavone, Mantero, Menale, Vanna, Cerullo, Sobacchi and Polli.)

We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broadband red-shifted Stokes pulses, covering the whole fingerprint region (400-1800 cm -1 ), employing supercontinuum generation in a bulk crystal. Our system reaches state-of-the-art acquisition speed (<1 ms/pixel) and unprecedented sensitivity of ≈14.1 mmol/L when detecting dimethyl sulfoxide in water. To further improve the performance of the system and to enhance the signal-to-noise ratio of the CARS spectra, we designed a convolutional neural network for spectral denoising, coupled with a post-processing pipeline to distinguish different chemical species of biological tissues.

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical technique for label-free chemical imaging. This analytical tool delivers chemical maps at high speed, and high spatial resolution of thin samples by directly interrogating their molecular vibrations. In its standard implementation, SRS microscopy is narrowband and forms images with only a single vibrational frequency at a time. However, this approach not only hinders the chemical specificity of SRS but also neglects the wealth of information encoded within vibrational spectra. These limitations can be overcome by broadband SRS, an implementation capable of extracting a vibrational spectrum per pixel of the image in parallel. This delivers hyperspectral data that, when coupled with chemometric analysis, maximizes the amount of information retrieved from the specimen. Thus, broadband SRS improves the chemical specificity of the system, allowing the quantitative determination of the concentration of the different constituents of a sample. Here, we report a protocol for chemical imaging with broadband SRS microscopy, based on a home-built SRS microscope operating with a custom differential multichannel-lock-in amplifier detection. It discusses the sample preparation, alignment of the SRS apparatus, and chemometric analysis. By acquiring vibrational Raman spectra, the protocol illustrates how to identify different chemical species within a mixture, determining their relative concentrations.

Canonical immunoassays rely on highly sensitive and specific capturing of circulating biomarkers by interacting biomolecular baits. In this frame, bioprobe immobilization in spatially discrete three-dimensional (3D) spots onto analytical surfaces by hydrogel encapsulation was shown to provide relevant advantages over conventional two-dimensional (2D) platforms. Yet, the broad application of 3D systems is still hampered by hurdles in matching their straightforward fabrication with optimal functional properties. Herein, we report on a composite hydrogel obtained by combining a self-assembling peptide (namely, Q3 peptide) with low-temperature gelling agarose that is proved to have simple and robust application in the fabrication of microdroplet arrays, overcoming hurdles and limitations commonly associated with 3D hydrogel assays. We demonstrate the real-case scenario feasibility of our 3D system in the profiling of Covid-19 patients' serum IgG immunoreactivity, which showed remarkably improved signal-to-noise ratio over canonical assays in the 2D format and exquisite specificity. Overall, the new two-component hydrogel widens the perspectives of hydrogel-based arrays and represents a step forward towards their routine use in analytical practices.