Chad A. Lieber

Automated method for subtraction of fluorescence from biological Raman spectra

One of the challenges of using Raman spectroscopy for biological applications is the inherent fluorescence generated by many biological molecules that underlies the measured spectra. This fluorescence can sometimes be several orders of magnitude more intense than the weak Raman scatter, and its presence must be minimized in order to resolve and analyze the Raman spectrum. Several techniques involving hardware and software have been devised for this purpose; these include the use of wavelength shifting, time gating, frequency-domain filtering, first- and second-order derivatives, and simple curve fitting of the broadband variation with a high-order polynomial. Of these, polynomial fitting has been found to be a simple but effective method. However, this technique typically requires user intervention and thus is time consuming and prone to variability. An automated method for fluorescence subtraction, based on a modification to least-squares polynomial curve fitting, is described. Results indicate that the presented automated method is proficient in fluorescence subtraction, repeatability, and in retention of Raman spectral lineshapes.

In vivo nonmelanoma skin cancer diagnosis using Raman microspectroscopy

Nonmelanoma skin cancers, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), are the most common skin cancers, presenting nearly as many cases as all other cancers combined. The current gold‐standard for clinical diagnosis of these lesions is histopathologic examination, an invasive, time‐consuming procedure. There is thus considerable interest in developing a real‐time, automated, noninvasive tool for nonmelanoma skin cancer diagnosis. In this study, we explored the capability of Raman microspectroscopy to provide differential diagnosis of BCC, SCC, inflamed scar tissue, and normal tissue in vivo.

Raman microspectroscopy for skin cancer detection in vitro

We investigate the potential of near-infrared Raman microspectroscopy to differentiate between normal and malignant skin lesions. Thirty-nine skin tissue samples consisting of normal, basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma from 39 patients were investigated. Raman spectra were recorded at the surface and at 20-microm intervals below the surface for each sample, down to a depth of at least 100 microm. Data reduction algorithms based on the nonlinear maximum representation and discrimination feature (MRDF) and discriminant algorithms using sparse multinomial logistic regression (SMLR) were developed for classification of the Raman spectra relative to histopathology. The tissue Raman spectra were classified into pathological states with a maximal overall sensitivity and specificity for disease of 100%. These results indicate the potential of using Raman microspectroscopy for skin cancer detection and provide a clear rationale for future clinical studies.

Detecting Temporal and Spatial Effects of Epithelial Cancers with Raman Spectroscopy

Epithelial cancers, including those of the skin and cervix, are the most common type of cancers in humans. Many recent studies have attempted to use Raman spectroscopy to diagnose these cancers. In this paper, Raman spectral markers related to the temporal and spatial effects of cervical and skin cancers are examined through four separate but related studies. Results from a clinical cervix study show that previous disease has a significant effect on the Raman signatures of the cervix, which allow for near 100% classification for discriminating previous disease versus a true normal. A Raman microspectroscopy study showed that Raman can detect changes due to adjacent regions of dysplasia or HPV that cannot be detected histologically, while a clinical skin study showed that Raman spectra may be detecting malignancy associated changes in tissues surrounding nonmelanoma skin cancers. Finally, results of an organotypic raft culture study provided support for both the skin and the in vitro cervix results. These studies add to the growing body of evidence that optical spectroscopy, in this case Raman spectral markers, can be used to detect subtle temporal and spatial effects in tissue near cancerous sites that go otherwise undetected by conventional histology.

Development of a handheld Raman microspectrometer for clinical dermatologic applications

Although skin is easily accessible to optical methodologies, a portable measurement head is necessary to allow ready spectroscopic interrogation of all anatomic locations. However, most conventional Raman microspectrometers and even dermatologic-specific Raman systems are fixed systems ill-suited to anatomic accessibility. To this end, we have developed a portable Raman microspectrometer system for future dermatologic studies. An in-house-built bench-top system was used to qualify the optical components and design. Based on this systems layout, a handheld microspectrometer was developed for future clinical application. This system produces similar operating characteristics to the bench-top prototype, and is shown to provide clear Raman spectra from skin tissue measured in vivo in clinically-feasible integration times.

Influence of tissue absorption and scattering on the depth dependent sensitivity of Raman fiber probes investigated by Monte Carlo simulations

We present a Monte Carlo model, which we use to calculate the depth dependent sensitivity or sampling volume of different single fiber and multi-fiber Raman probes. A two-layer skin model is employed to investigate the dependency of the sampling volume on the absorption and reduced scattering coefficients in the near infrared wavelength range (NIR). The shape of the sampling volume is mainly determined by the scattering coefficient and the wavelength dependency of absorption and scattering has only a small effect on the sampling volume of a typical fingerprint spectrum. An increase in the sampling depth in nonmelanoma skin cancer, compared to normal skin, is obtained.

1064 nm dispersive Raman spectroscopy of tissues with strong near-infrared autofluorescence

Raman spectroscopy is an established technique for molecularly specific characterization of tissues. However, even with near-infrared (NIR) excitation, some tissues possess background autofluorescence, which can overwhelm Raman scattering. Here, we report collection of spectra from tissues with strong autofluorescence using a 1064 nm system with a high-throughput dispersive spectrometer and deep-cooled InGaAs array. Spectra collected at 1064 nm were compared with those collected at 785 nm in specimens from human breast, liver, and kidney. The results demonstrate superior performance at 1064 nm in the liver and kidney, where NIR autofluorescence is intense. The results indicate the feasibility of new biomedical applications for Raman spectroscopy at 1064 nm in tissues with strong autofluorescence.

Cancer field effects in normal tissues revealed by Raman spectroscopy.

It has been demonstrated that the presence of cancer results in detectable changes to uninvolved tissues, collectively termed cancer field effects (CFE). In this study, we directly assessed the ability of Raman microspectroscopy to detect CFE via in-vitro study of organotypic tissue rafts approximating human skin. Raman spectra were measured from both epidermis and dermis after transfer of the rafts to dishes containing adherent cultures of either normal human fibroblasts or fibrosarcoma (HT1080) cells. Principal components analyses allowed discrimination between the groups with 86% classification accuracy in the epidermis and 94% in the dermis. These results encourage further study to evaluate the Raman capacity for detecting CFE as a possible tool for noninvasive screening for tumor presence.

Discrimination of Liver Malignancies with 1064 nm Dispersive Raman Spectroscopy

Raman spectroscopy has been widely demonstrated for tissue characterization and disease discrimination, however current implementations with either 785 or 830 nm near-infrared (NIR) excitation have been ineffectual in tissues with intense autofluorescence such as the liver. Here we report the use of a dispersive 1064 nm Raman system using a low-noise Indium-Gallium-Arsenide (InGaAs) array to discriminate highly autofluorescent bulk tissue ex vivo specimens from healthy liver, adenocarcinoma, and hepatocellular carcinoma (N = 5 per group). The resulting spectra have been combined with a multivariate discrimination algorithm, sparse multinomial logistic regression (SMLR), to predict class membership of healthy and diseased tissues, and spectral bands selected for robust classification have been extracted. A quantitative metric called feature importance is defined based on classification outputs and is used to guide the association of spectral features with biological indicators of healthy and diseased liver tissue. Spectral bands with high feature importance for healthy and liver tumor specimens include retinol, heme, biliverdin, or quinones (1595 cm(-1)); lactic acid (838 cm(-1)); collagen (873 cm(-1)); and nucleic acids (1485 cm(-1)). Classification performance in both binary (normal versus tumor, 100% sensitivity and 89% specificity) and three-group cases (classification accuracy: normal 89%, adenocarcinoma 74%, hepatocellular carcinoma 64%) indicates the potential for accurately separating healthy and cancerous tissues and suggests implications for utilizing Raman techniques during surgical guidance in liver resection.

Comparison of Raman Spectrograph Throughput Using Two Commercial Systems: Transmissive versus Reflective

A common goal of most Raman spectroscopists is the acquisition of clear Raman spectra in short integration times. It is paramount, then, that the hardware used in a Raman system be optimized for throughput, signal-to-noise ratio, and resolution. To this end, we performed a systematic comparison of two Raman spectrographs: the widely used Kaiser HoloSpec™ and a relatively new commercial offering, the Raman Explorer™ by Headwall Photonics. Both strong and weak Raman scattering samples were measured using various launching conditions. When resolution-matched, the throughputs of both spectrographs were found to be roughly similar over the central range of the fingerprint region (approximately 800 to 1140 cm−1, using 785 nm illumination), with the Raman Explorer™ demonstrating a slight throughput advantage outside this range. Other factors are also considered such that end users may better select the optimum spectrograph for their particular application.