Translating microcalcification biomarker information into the laboratory: A preliminary assessment utilizing core biopsies obtained from sites of mammographic calcification.

Rationale and objectives: The potential of breast microcalcification chemistry to provide clinically valuable intelligence is being increasingly studied. However, acquisition of crystallographic details has, to date, been limited to high brightness, synchrotron radiation sources. This study, for the first time, evaluates a laboratory-based system that interrogates histological sections containing microcalcifications. The principal objective was to determine the measurement precision of the laboratory system and assess whether this was sufficient to provide potentially clinical valuable information. Materials and methods: Sections from 5 histological specimens from breast core biopsies obtained to evaluate mammographic calcification were examined using a synchrotron source and a laboratory-based instrument. The samples were chosen to represent a significant proportion of the known breast tissue, mineralogical landscape. Data were subsequently analysed using conventional methods and microcalcification characteristics such as crystallographic phase, chemical deviation from ideal stoichiometry and microstructure were determined. Results: The crystallographic phase of each microcalcification (e.g., hydroxyapatite, whitlockite) was easily determined from the laboratory derived data even when a mixed phase was apparent. Lattice parameter values from the laboratory experiments agreed well with the corresponding synchrotron values and, critically, were determined to precisions that were significantly greater than required for potential clinical exploitation. Conclusion: It has been shown that crystallographic characteristics of microcalcifications can be determined in the laboratory with sufficient precision to have potential clinical value. The work will thus enable exploitation acceleration of these latent microcalcification features as current dependence upon access to limited synchrotron resources is minimized.

Stream-disk shocks as the origins of peak light in tidal disruption events.

Tidal disruption events (TDEs) occur when stars are ripped apart1,2 by massive black holes and result in highly luminous, multi-wavelength flares3-5. Optical-ultraviolet observations5-7 of TDEs contradict simple models of TDE emission2,8, but the debate between alternative models (for example, shock power9,10 or reprocessed accretion power11-16) remains unsettled, as the dynamic range of the problem has so far prevented ab initio hydrodynamical simulations17. Consequently, past simulations have resorted to unrealistic parameter choices10,12,18-21, artificial mass injection schemes22,23 or very short run-times24. Here we present a three-dimensional radiation-hydrodynamic simulation of a TDE flare from disruption to peak emission, with typical astrophysical parameters. At early times, shocks near pericentre power the light curve and a previously unknown source of X-ray emission, but circularization and outflows are inefficient. Near peak light, stream-disk shocks efficiently circularize returning debris, power stronger outflows and reproduce observed peak optical-ultraviolet luminosities25,26. Peak emission in this simulation is shock-powered, but upper limits on accretion power become competitive near peak light as circularization runs away. This simulation shows how deterministic predictions of TDE light curves and spectra can be calculated using moving-mesh hydrodynamics algorithms.

Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte.

There is increasing interest in the application of Raman spectroscopy in a medical setting, ranging from supporting real-time clinical decisions e.g. surgical margins to assisting pathologists with disease classification. However, there remain a number of barriers for adoption in the medical setting due to the increased complexity of probing highly heterogeneous, dynamic biological materials. This inherent challenge can also limit the deployment of higher level analytical approaches such as Artificial Intelligence (AI) including convolutional neural networks (CNN), as there is a lack of a ground truth required for training purposes i.e. in complex clinical samples. Principal component analysis (PCA) is an unsupervised data reduction approach (orthogonal linear transformation) that has been used extensively in spectroscopy for 30+ years, due to its capability to simplify analysis of complex spectroscopic data. However, due to PCA being unsupervised features will inherently appear mixed and their rank may vary between experiments. Here we propose Guided PCA (GPCA), a simple approach that allows PCA to be guided with spectral data to ensure a consistent rank of a key target moiety by the inclusion of a reference (guiding) spectrum to the data set. This simplifies analysis, increases robustness of PCA analysis and improves quantification and the limits of detection and decreases RMSE.

Label-free microfluidic isolation of functional and viable lymphocytes from peripheral blood mononuclear cells.

The separation of peripheral blood mononuclear cells (PBMCs) into constituent blood cell types is a vital step to obtain immune cells for autologous cell therapies. The ability to separate PBMCs using label-free microfluidic techniques, based on differences in biomechanical properties, can have a number of benefits over other conventional techniques, including lower cost, ease of use, and avoidance of animal-derived labeling antibodies. Here, we report a microfluidic device that uses compressive diagonal ridges to separate PBMCs into highly pure samples of viable and functional lymphocytes. The technique utilizes the differences in the biophysical properties of PBMC sub-populations to direct the lymphocytes and monocytes into separate outlets. The biophysical properties of the monocytes and lymphocytes from healthy donors were first characterized using atomic force microscopy. Lymphocytes were found to be significantly stiffer than monocytes, with a mean cell stiffness of 1495 and 931 Pa, respectively. The differences in biophysical properties resulted in distinct trajectories through the microchannel terminating at different outlets, resulting in a lymphocyte sample with purity and viability both greater than 96% with no effect on the cells' ability to produce interferon gamma, a cytokine crucial for innate and adaptive immunity.

CGMP Compliant Microfluidic Transfection of Induced Pluripotent Stem Cells for CRISPR-Mediated Genome Editing.

Inherited retinal degeneration is a term used to describe heritable disorders that result from the death of light sensing photoreceptor cells. Although we and others believe that it will be possible to use gene therapy to halt disease progression early in its course, photoreceptor cell replacement will likely be required for patients who have already lost their sight. While advances in autologous photoreceptor cell manufacturing have been encouraging, development of technologies capable of efficiently delivering genome editing reagents to stem cells using current good manufacturing practices (cGMP) are needed. Gene editing reagents were delivered to induced pluripotent stem cells (iPSCs) using a Zephyr microfluidic transfection platform (CellFE). CRISPR-mediated cutting was quantified using an endonuclease assay. CRISPR correction was confirmed via digital PCR and Sanger sequencing. The resulting corrected cells were also karyotyped and differentiated into retinal organoids. We describe use of a novel microfluidic transfection platform to correct, via CRISPR-mediated homology-dependent repair (HDR), a disease-causing NR2E3 mutation in patient-derived iPSCs using cGMP compatible reagents and approaches. We show that the resulting cell lines have a corrected genotype, exhibit no off-target cutting, retain pluripotency and a normal karyotype and can be differentiated into retinal tissue suitable for transplantation. The ability to codeliver CRISPR/Cas9 and HDR templates to patient-derived iPSCs without using proprietary transfection reagents will streamline manufacturing protocols, increase the safety of resulting cell therapies, and greatly reduce the regulatory burden of clinical trials.

Automated human induced pluripotent stem cell colony segmentation for use in cell culture automation applications.

Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellXTM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellXTM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.

Microcalcification crystallography as a potential marker of DCIS recurrence.

Ductal carcinoma in-situ (DCIS) accounts for 20-25% of all new breast cancer diagnoses. DCIS has an uncertain risk of progression to invasive breast cancer and a lack of predictive biomarkers may result in relatively high levels (~ 75%) of overtreatment. To identify unique prognostic biomarkers of invasive progression, crystallographic and chemical features of DCIS microcalcifications have been explored. Samples from patients with at least 5-years of follow up and no known recurrence (174 calcifications in 67 patients) or ipsilateral invasive breast cancer recurrence (179 microcalcifications in 57 patients) were studied. Significant differences were noted between the two groups including whitlockite relative mass, hydroxyapatite and whitlockite crystal maturity and, elementally, sodium to calcium ion ratio. A preliminary predictive model for DCIS to invasive cancer progression was developed from these parameters with an AUC of 0.797. These results provide insights into the differing DCIS tissue microenvironments, and how these impact microcalcification formation.

Propensity of Patient-Derived iPSCs for Retinal Differentiation: Implications for Autologous Cell Replacement.

Prior to use, newly generated induced pluripotent stem cells (iPSC) should be thoroughly validated. While excellent validation and release testing assays designed to evaluate potency, genetic integrity, and sterility exist, they do not have the ability to predict cell type-specific differentiation capacity. Selection of iPSC lines that have limited capacity to produce high-quality transplantable cells, places significant strain on valuable clinical manufacturing resources. The purpose of this study was to determine the degree and root cause of variability in retinal differentiation capacity between cGMP-derived patient iPSC lines. In turn, our goal was to develop a release testing assay that could be used to augment the widely used ScoreCard panel. IPSCs were generated from 15 patients (14-76 years old), differentiated into retinal organoids, and scored based on their retinal differentiation capacity. Despite significant differences in retinal differentiation propensity, RNA-sequencing revealed remarkable similarity between patient-derived iPSC lines prior to differentiation. At 7 days of differentiation, significant differences in gene expression could be detected. Ingenuity pathway analysis revealed perturbations in pathways associated with pluripotency and early cell fate commitment. For example, good and poor producers had noticeably different expressions of OCT4 and SOX2 effector genes. QPCR assays targeting genes identified via RNA sequencing were developed and validated in a masked fashion using iPSCs from 8 independent patients. A subset of 14 genes, which include the retinal cell fate markers RAX, LHX2, VSX2, and SIX6 (all elevated in the good producers), were found to be predictive of retinal differentiation propensity.

10†kHz Shifted-Excitation Raman Difference Spectroscopy with Charge-Shifting Charge-Coupled Device Read-Out for Effective Mitigation of Dynamic Interfering Backgrounds.

In this work we demonstrate an advanced concept of a charge-shifting charge-coupled device (CCD) read-out combined with shifted excitation Raman difference spectroscopy (SERDS) capable of operating at up to 10 kHz acquisition rates for the effective mitigation of fast-evolving interfering backgrounds in Raman spectroscopy. This rate is 10-fold faster than that achievable with an instrument we described previously and is overall 1000-fold faster than possible with conventional spectroscopic CCDs capable of operating at up to ∼10 Hz rates. The speed enhancement was realized by incorporating a periodic mask at the internal slit of an imaging spectrometer permitting a smaller shift of the charge on the CCD (8 pixels) to be required during the cyclic shifting process compared with the earlier design which employed an 80-pixel shift. The higher acquisition speed enables the more accurate sampling of the two SERDS spectral channels, enabling it to effectively tackle highly challenging situations with rapidly evolving interfering fluorescence backgrounds. The performance of the instrument is evaluated for heterogeneous fluorescent samples which are moved rapidly in front of the detection system aiming at the differentiation of chemical species and their quantification. The performance of the system is compared with that of the earlier 1 kHz design and a conventional CCD operated at its maximum rate of 5.4 Hz as previously. In all situations tested, the newly developed 10 kHz system outperformed the earlier variants. The 10 kHz instrument can benefit a number of prospective applications including: disease diagnosis where high sensitivity mapping of complex biological matrices in the presence of natural fluorescence bleaching restricts achievable limits of detection; accurate data acquisition from moving heterogeneous samples (or moving a handheld instrument in front of the sample during data acquisition) or data acquisition under varying ambient light conditions (e.g., due to casting shadows, sample or instrument movement). Other beneficial scenarios include monitoring rapidly evolving Raman signals in the presence of largely static background signals such as in situations where a heterogeneous sample is moving rapidly in front of a detection system (e.g., a conveyor belt) in the presence of static ambient light.

Automating iPSC generation to enable autologous photoreceptor cell replacement therapy.

BACKGROUND: Inherited retinal degeneration is a leading cause of incurable vision loss in the developed world. While autologous iPSC mediated photoreceptor cell replacement is theoretically possible, the lack of commercially available technologies designed to enable high throughput parallel production of patient specific therapeutics has hindered clinical translation. METHODS: In this study, we describe the use of the Cell X precision robotic cell culture platform to enable parallel production of clinical grade patient specific iPSCs. The Cell X is housed within an ISO Class 5 cGMP compliant closed aseptic isolator (Biospherix XVivo X2), where all procedures from fibroblast culture to iPSC generation, clonal expansion and retinal differentiation were performed. RESULTS: Patient iPSCs generated using the Cell X platform were determined to be pluripotent via score card analysis and genetically stable via karyotyping. As determined via immunostaining and confocal microscopy, iPSCs generated using the Cell X platform gave rise to retinal organoids that were indistinguishable from organoids derived from manually generated iPSCs. In addition, at 120 days post-differentiation, single-cell RNA sequencing analysis revealed that cells generated using the Cell X platform were comparable to those generated under manual conditions in a separate laboratory. CONCLUSION: We have successfully developed a robotic iPSC generation platform and standard operating procedures for production of high-quality photoreceptor precursor cells that are compatible with current good manufacturing practices. This system will enable clinical grade production of iPSCs for autologous retinal cell replacement.

Analysis of the Chemical Distribution of Self-Assembled Microdomains with the Selective Localization of Amine-Functionalized Graphene Nanoplatelets by Optical Photothermal Infrared Microspectroscopy.

By incorporating 1-(2-aminoethyl)piperazine (AEPIP) into a commercial epoxy blend, a bicontinuous microstructure is produced with the selective localization of amine-functionalized graphene nanoplatelets (A-GNPs). This cured blend underwent self-assembly, and the morphology and topology were observed via spectral imaging techniques. As the selective localization of nanofillers in thermoset blends is rarely achieved, and the mechanism remains largely unknown, the optical photothermal infrared (O-PTIR) spectroscopy technique was employed to identify the compositions of microdomains. The A-GNP tends to be located in the region containing higher concentrations of both secondary amine and secondary alcohol; additionally, the phase morphology was found to be influenced by the amine concentration. With the addition of AEPIP, the size of the graphene domains becomes smaller and secondary phase separation is detected within the graphene domain evidenced by the chemical contrast shown in the high-resolution chemical map. The corresponding chemical mapping clearly shows that this phenomenon was mainly induced by the chemical contrast in related regions. The findings reported here provide new insight into a complicated, self-assembled nanofiller domain formed in a multicomponent epoxy blend, demonstrating the potential of O-PTIR as a powerful and useful approach for assessing the mechanism of selectively locating nanofillers in the phase structure of complex thermoset systems.

Breast calcification micromorphology classification.

OBJECTIVES: The importance of consistent terminology in describing the appearance of breast calcifications in mammography is well recognised. Imaging of calcifications using electron microscopy is a globally growing field of research. We therefore suggest that the time is ripe to develop a lexicon of terms for classifying the micromorphology of breast calcifications. METHODS: Calcifications within a wide range of histological sections of breast tissue, both benign and malignant, were imaged by Scanning Electron Microscopy (SEM). These images were examined, and the micromorphology of calcifications present was grouped to create a classification system. RESULTS: Based on the appearance of the calcifications observed, we propose five main categories for classification of the micromorphology of breast calcifications, namely, Dense Homogenous, Punctulate, Banded, Spongy and Aggregate. CONCLUSIONS: Use of the descriptive categories outlined here will help to ensure consistency and comparability of published observations on the micromorphology of breast calcifications. ADVANCES IN KNOWLEDGE: This is the first time a lexicon and classification system has been proposed for the micromorphology of breast calcifications, as observed by scanning electron microscopy of histological sections. This will facilitate comparability of observed relationships between micromorphology, mammographic appearance, chemistry and pathology.

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy.

Probing gold nanoparticles (AuNPs) in living systems is essential to reveal the interaction between AuNPs and biological tissues. Moreover, by integrating nonlinear optical signals such as stimulated Raman scattering (SRS), two-photon excited fluorescence (TPEF), and transient absorption (TA) into an imaging platform, it can be used to reveal biomolecular contrast of cellular structures and AuNPs in a multimodal manner. This article presents a multimodal nonlinear optical microscopy and applies it to perform chemically specific imaging of AuNPs in cancer cells. This imaging platform provides a novel approach for developing more efficient functionalized AuNPs and determining whether they are within vasculatures surrounding the tumor, pericellular, or cellular spaces.

A multi-modal exploration of heterogeneous physico-chemical properties of DCIS breast microcalcifications.

Ductal carcinoma in situ (DCIS) is frequently associated with breast calcification. This study combines multiple analytical techniques to investigate the heterogeneity of these calcifications at the micrometre scale. X-ray diffraction, scanning electron microscopy and Raman and Fourier-transform infrared spectroscopy were used to determine the physicochemical and crystallographic properties of type II breast calcifications located in formalin fixed paraffin embedded DCIS breast tissue samples. Multiple calcium phosphate phases were identified across the calcifications, distributed in different patterns. Hydroxyapatite was the dominant mineral, with magnesium whitlockite found at the calcification edge. Amorphous calcium phosphate and octacalcium phosphate were also identified close to the calcification edge at the apparent mineral/matrix barrier. Crystallographic features of hydroxyapatite also varied across the calcifications, with higher crystallinity centrally, and highest carbonate substitution at the calcification edge. Protein was also differentially distributed across the calcification and the surrounding soft tissue, with collagen and ß-pleated protein features present to differing extents. Combination of analytical techniques in this study was essential to understand the heterogeneity of breast calcifications and how this may link crystallographic and physicochemical properties of calcifications to the surrounding tissue microenvironment.

Nanoparticle-Mediated Photothermal Therapy Limitation in Clinical Applications Regarding Pain Management.

The development of new effective cancer treatment methods has attracted much attention, mainly due to the limited efficacy and considerable side effects of currently used cancer treatment methods such as radiation therapy and chemotherapy. Photothermal therapy based on the use of plasmonically resonant metallic nanoparticles has emerged as a promising technique to eradicate cancer cells selectively. In this method, plasmonic nanoparticles are first preferentially uptaken by a tumor and then selectively heated by exposure to laser radiation with a specific plasmonic resonant wavelength, to destroy the tumor whilst minimizing damage to adjacent normal tissue. However, several parameters can limit the effectiveness of photothermal therapy, resulting in insufficient heating and potentially leading to cancer recurrence. One of these parameters is the patient's pain sensation during the treatment, if this is performed without use of anesthetic. Pain can restrict the level of applicable laser radiation, cause an interruption to the treatment course and, as such, affect its efficacy, as well as leading to a negative patient experience and consequential general population hesitancy to this type of therapy. Since having a comfortable and painless procedure is one of the important treatment goals in the clinic, along with its high effectiveness, and due to the relatively low number of studies devoted to this specific topic, we have compiled this review. Moreover, non-invasive and painless methods for temperature measurement during photothermal therapy (PTT), such as Raman spectroscopy and nanothermometry, will be discussed in the following. Here, we firstly outline the physical phenomena underlying the photothermal therapy, and then discuss studies devoted to photothermal cancer treatment concerning pain management and pathways for improved efficiency of photothermal therapy whilst minimizing pain experienced by the patient.

Cryo-EM structures reveal high-resolution mechanism of a DNA polymerase sliding clamp loader.

Sliding clamps are ring-shaped protein complexes that are integral to the DNA replication machinery of all life. Sliding clamps are opened and installed onto DNA by clamp loader AAA+ ATPase complexes. However, how a clamp loader opens and closes the sliding clamp around DNA is still unknown. Here, we describe structures of the Saccharomyces cerevisiae clamp loader Replication Factor C (RFC) bound to its cognate sliding clamp Proliferating Cell Nuclear Antigen (PCNA) en route to successful loading. RFC first binds to PCNA in a dynamic, closed conformation that blocks both ATPase activity and DNA binding. RFC then opens the PCNA ring through a large-scale 'crab-claw' expansion of both RFC and PCNA that explains how RFC prefers initial binding of PCNA over DNA. Next, the open RFC:PCNA complex binds DNA and interrogates the primer-template junction using a surprising base-flipping mechanism. Our structures indicate that initial PCNA opening and subsequent closure around DNA do not require ATP hydrolysis, but are driven by binding energy. ATP hydrolysis, which is necessary for RFC release, is triggered by interactions with both PCNA and DNA, explaining RFC's switch-like ATPase activity. Our work reveals how a AAA+ machine undergoes dramatic conformational changes for achieving binding preference and substrate remodeling.

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ2 test. Results The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning. © RSNA, 2022.

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders.

Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.

An experimental and numerical modelling investigation of the optical properties of Intralipid using deep Raman spectroscopy.

In this study, Monte Carlo simulations were created to investigate the distribution of Raman signals in tissue phantoms and to validate the arctk code that was used. The aim was to show our code is capable of replicating experimental results in order to use it to advise similar future studies and to predict the outcomes. The experiment performed to benchmark our code used large volume liquid tissue phantoms to simulate the scattering properties of human tissue. The scattering agent used was Intralipid (IL), of various concentrations, filling a small quartz tank. A thin sample of PTFE was made to act as a distinct layer in the tank; this was our Raman signal source. We studied experimentally, and then reproduced via simulations, the variation in Raman signal strength in a transmission geometry as a function of the optical properties of the scattering agent and the location of the Raman material in the volume. We have also found that a direct linear extrapolation of scattering coefficients between concentrations of Intralipid is an incorrect assumption at lower concentrations when determining the optical properties. By combining experimental and simulation results, we have calculated different estimates of these scattering coefficients. The results of this study give insight into light propagation and Raman transport in scattering media and show how the location of maximum Raman signal varies as the optical properties change. The success of arctk in reproducing observed experimental signal behaviour will allow us in future to inform the development of noninvasive cancer screening applications (such as breast and prostate cancers) in vivo.

Label-free microfluidic enrichment of cancer cells from non-cancer cells in ascites.

The isolation of a patient's metastatic cancer cells is the first, enabling step toward treatment of that patient using modern personalized medicine techniques. Whereas traditional standard-of-care approaches select treatments for cancer patients based on the histological classification of cancerous tissue at the time of diagnosis, personalized medicine techniques leverage molecular and functional analysis of a patient's own cancer cells to select treatments with the highest likelihood of being effective. Unfortunately, the pure populations of cancer cells required for these analyses can be difficult to acquire, given that metastatic cancer cells typically reside in fluid containing many different cell populations. Detection and analyses of cancer cells therefore require separation from these contaminating cells. Conventional cell sorting approaches such as Fluorescence Activated Cell Sorting or Magnetic Activated Cell Sorting rely on the presence of distinct surface markers on cells of interest which may not be known nor exist for cancer applications. In this work, we present a microfluidic platform capable of label-free enrichment of tumor cells from the ascites fluid of ovarian cancer patients. This approach sorts cells based on differences in biomechanical properties, and therefore does not require any labeling or other pre-sort interference with the cells. The method is also useful in the cases when specific surface markers do not exist for cells of interest. In model ovarian cancer cell lines, the method was used to separate invasive subtypes from less invasive subtypes with an enrichment of ~ sixfold. In ascites specimens from ovarian cancer patients, we found the enrichment protocol resulted in an improved purity of P53 mutant cells indicative of the presence of ovarian cancer cells. We believe that this technology could enable the application of personalized medicine based on analysis of liquid biopsy patient specimens, such as ascites from ovarian cancer patients, for quick evaluation of metastatic disease progression and determination of patient-specific treatment.