High-speed spectral nanocytology for early cancer screening

Early detection of lung cancer using artificial intelligence-enhanced optical nanosensing of chromatin alterations in field carcinogenesis

Supranucleosomal chromatin structure, including chromatin domain conformation, is involved in the regulation of gene expression and its dysregulation has been associated with carcinogenesis. Prior studies have shown that cells in the buccal mucosa carry a molecular signature of lung cancer among the cigarette-smoking population, the phenomenon known as field carcinogenesis or field of injury. Thus, we hypothesized that chromatin structural changes in buccal mucosa can be predictive of lung cancer. However, the small size of the chromatin chain (approximately 20 nm) folded into chromatin packing domains, themselves typically below 300 nm in diameter, preclude the detection of alterations in intradomain chromatin conformation using diffraction-limited optical microscopy. In this study, we developed an optical spectroscopic statistical nanosensing technique to detect chromatin packing domain changes in buccal mucosa as a lung cancer biomarker: chromatin-sensitive partial wave spectroscopic microscopy (csPWS). Artificial intelligence (AI) was applied to csPWS measurements of chromatin alterations to enhance diagnostic performance. Our AI-enhanced buccal csPWS nanocytology of 179 patients at two clinical sites distinguished Stage-I lung cancer versus cancer-free controls with an area under the ROC curve (AUC) of 0.92 ± 0.06 for Site 1 (in-state location) and 0.82 ± 0.11 for Site 2 (out-of-state location).

Assessment of internal refractive index profile of stochastically inhomogeneous nuclear models via analysis of two-dimensional optical scattering patterns

SIGNIFICANCE

Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations.

AIM

We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering.

APPROACH

We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure.

RESULTS

Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution.

CONCLUSIONS

Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Tissue spatial correlation as cancer marker

We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial autocorrelation length obtained from quantitative phase images. The spatial autocorrelation length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation length maps are calculated by computing two-dimensional Fourier transforms over image subregions-requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.

Label free localization of nanoparticles in live cancer cells using spectroscopic microscopy

Gold nanoparticles (GNPs) have become essential tools used in nanobiotechnology due to their tunable plasmonic properties and low toxicity in biological samples. Among the available approaches for imaging GNPs internalized by cells, hyperspectral techniques stand out due to their ability to simultaneously image and perform spectral analysis of GNPs. Here, we present a study utilizing a recently introduced hyperspectral imaging technique, live-cell PWS, for the imaging, tracking, and spectral analysis of GNPs in live cancer cells. Using principal components analysis, the extracellular or intracellular localization of the GNPs can be determined without the use of exogenous labels. This technique uses wide-field white light, assuring minimal toxicity and suitable signal-to-noise ratio for spectral and temporal resolution of backscattered signal from GNPs and local cellular structures. The application of live-cell PWS introduced here could make a great impact in nanomedicine and nanotechnology by giving new insights into GNP internalization and intracellular trafficking.

Disorder strength measured by quantitative phase imaging as intrinsic cancer marker in fixed tissue biopsies

Tissue refractive index provides important information about morphology at the nanoscale. Since the malignant transformation involves both intra- and inter-cellular changes in the refractive index map, the tissue disorder measurement can be used to extract important diagnosis information. Quantitative phase imaging (QPI) provides a practical means of extracting this information as it maps the optical path-length difference (OPD) across a tissue sample with sub-wavelength sensitivity. In this work, we employ QPI to compare the tissue disorder strength between benign and malignant breast tissue histology samples. Our results show that disease progression is marked by a significant increase in the disorder strength. Since our imaging system can be added as an upgrading module to an existing microscope, we anticipate that it can be integrated easily in the pathology work flow.

Biophotonic detection of high order chromatin alterations in field carcinogenesis predicts risk of future hepatocellular carcinoma: A pilot study

PURPOSE

Hepatocellular carcinoma (HCC) results from chronic inflammation/cirrhosis. Unfortunately, despite use of radiological/serological screening techniques, HCC ranks as a leading cause of cancer deaths. Our group has used alterations in high order chromatin as a marker for field carcinogenesis and hence risk for a variety of cancers (including colon, lung, prostate, ovarian, esophageal). In this study we wanted to address whether these chromatin alterations occur in HCC and if it could be used for risk stratification.

EXPERIMENTAL DESIGN

A case control study was performed in patients with cirrhosis who went on to develop HCC and patients with cirrhosis who did not develop cancer. We performed partial wave spectroscopic microscopy (PWS) which measures nanoscale alterations on formalin fixed deparaffinized liver biopsy specimens, 17 progressors and 26 non-progressors. Follow up was 2089 and 2892 days, respectively.

RESULTS

PWS parameter disorder strength Ld were notably higher for the progressors (Ld = 1.47 ± 0.76) than the non-progressors (Ld = 1.00 ± 0.27) (p = 0.024). Overall, the Cohen's d effect size was 0.907 (90.7%). AUROC analysis yielded an area of 0.70. There was no evidence of confounding by gender, age, BMI, smoking status and race.

CONCLUSIONS

High order chromatin alterations, as detected by PWS, is altered in pre-malignant hepatocytes with cirrhosis and may predict future risk of HCC.

The transformation of the nuclear nanoarchitecture in human field carcinogenesis

Morphological alterations of the nuclear texture are a hallmark of carcinogenesis. At later stages of disease, these changes are well characterized and detectable by light microscopy. Evidence suggests that similar albeit nanoscopic alterations develop at the predysplastic stages of carcinogenesis. Using the novel optical technique partial wave spectroscopic microscopy, we identified profound changes in the nanoscale chromatin topology in microscopically normal tissue as a common event in the field carcinogenesis of many cancers. In particular, higher-order chromatin structure at supranucleosomal length scales (20-200 nm) becomes exceedingly heterogeneous, a measure we quantify using the disorder strength (L_d ) of the spatial arrangement of chromatin density. Here, we review partial wave spectroscopic nanocytology clinical studies and the technology's promise as an early cancer screening technology.

Procedures for risk-stratification of lung cancer using buccal nanocytology

Lung cancer is the leading cause of cancer deaths in the U.S. with survival dramatically depending on stage at diagnosis. We had earlier reported that nanocytology of buccal cells can accurately risk-stratify smokers for the presence of early and late-stage lung cancer. To translate the technique into clinical practice, standardization of operating procedures is necessary to consistently yield precise and repeatable results. Here, we develop and validate simple, robust, and easily implementable procedures for specimen collection, processing, etc. in addition to a commercially-viable instrument prototype. Results of this work enable translation of the technology from academic lab to physicians' office.

Finite-difference time-domain-based optical microscopy simulation of dispersive media facilitates the development of optical imaging techniques

Combining finite-difference time-domain (FDTD) methods and modeling of optical microscopy modalities, we previously developed an open-source software package called Angora, which is essentially a “microscope in a computer.” However, the samples being simulated were limited to nondispersive media. Since media dispersions are common in biological samples (such as cells with staining and metallic biomarkers), we have further developed a module in Angora to simulate samples having complicated dispersion properties, thereby allowing the synthesis of microscope images of most biological samples. We first describe a method to integrate media dispersion into FDTD, and we validate the corresponding Angora dispersion module by applying Mie theory, as well as by experimentally imaging gold microspheres. Then, we demonstrate how Angora can facilitate the development of optical imaging techniques with a case study.

Nanoscale refractive index fluctuations detected via sparse spectral microscopy

Partial Wave Spectroscopic (PWS) Microscopy has proven effective at detecting nanoscale hallmarks of carcinogenesis in histologically normal-appearing cells. The current method of data analysis requires acquisition of a three-dimensional data cube, consisting of multiple images taken at different illumination wavelengths, limiting the technique to data acquisition on ~30 individual cells per slide. To enable high throughput data acquisition and whole-slide imaging, new analysis procedures were developed that require fewer wavelengths in the same 500-700nm range for spectral analysis. The nanoscale sensitivity of the new analysis techniques was validated (i) theoretically, using finite-difference time-domain solutions of Maxwell's equations, as well as (ii) experimentally, by measuring nanostructural alterations associated with carcinogenesis in biological cells.

Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology

Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.

Reconstruction of explicit structural properties at the nanoscale via spectroscopic microscopy

The spectrum registered by a reflected-light bright-field spectroscopic microscope (SM) can quantify the microscopically indiscernible, deeply subdiffractional length scales within samples such as biological cells and tissues. Nevertheless, quantification of biological specimens via any optical measures most often reveals ambiguous information about the specific structural properties within the studied samples. Thus, optical quantification remains nonintuitive to users from the diverse fields of technique application. In this work, we demonstrate that the SM signal can be analyzed to reconstruct explicit physical measures of internal structure within label-free, weakly scattering samples: characteristic length scale and the amplitude of spatial refractive-index (RI) fluctuations. We present and validate the reconstruction algorithm via finite-difference time-domain solutions of Maxwell's equations on an example of exponential spatial correlation of RI. We apply the validated algorithm to experimentally measure structural properties within isolated cells from two genetic variants of HT29 colon cancer cell line as well as within a prostate tissue biopsy section. The presented methodology can lead to the development of novel biophotonics techniques that create two-dimensional maps of explicit structural properties within biomaterials: the characteristic size of macromolecular complexes and the variance of local mass density.