High-Throughput Screening Raman Spectroscopy Platform for Label

Subscriber access provided by UNIV OF OREGON

Article

High-throughput screening Raman spectroscopy (HTS-RS) platform for label-free cellomics Iwan W Schie, Jan Rüger, Abdullah Saif Mondol, Anuradha Ramoji, Ute Neugebauer, Christoph Krafft, and Juergen Popp Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04127 • Publication Date (Web): 29 Dec 2017 Downloaded from http://pubs.acs.org on December 31, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

High-throughput screening Raman spectroscopy (HTS-RS) platform for label-free cellomics Iwan W. Schie1,*,†, Jan Rüger1,*, Abdullah S. Mondol1, Anuradha Ramoji1,2, Ute Neugebauer1,2,3, Christoph Krafft1, and Jürgen Popp1,2,3,† 1. Leibniz Institute of Photonic Technology Jena, Germany 2. Center for Sepsis Control and Care (CSCC), Jena University Hospital, Jena, Germany 3. Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller University Jena, Germany * Equally contribution authors † Corresponding Authors: [email protected]; [email protected]

Abstract We present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable a rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications. The potential of the new approach is illustrated by two applications: 1. HTS-RS-based differential white blood cell count. A classification model was trained using Raman spectra of 52218 lymphocytes, 48220 neutrophils, and 7294 monocytes from four volunteers. The model was applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. 2. HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9 and 1:99 mixtures of Panc1 cells and leukocytes yielded ratios of 55:45, 10:90 and 3:97, respectively. Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics.

Introduction The combination of automated microscopy with image analysis applied to drug discovery and cell biology is known as high-throughput screening (HTS), high-content screening (HCS), and is frequently referred to as cellomics.1–4 HTS has received considerable attention in biomedical research and drug discovery, providing a large set of parameters about complex biological systems, such as eukaryotic cells. It provides information on a large parameter set of a biological system, using morphological information, such as cell size, shape, granularity, or changes thereof; and information from fluorescent labeling of cellular proteins or organelles measured by automated image analysis.5 A key disadvantage for HTS is the requirement for fluorescent labels, which can exhibit cytotoxic effects, do photobleach, only allow a limited number of stains, due to spectral overlap, and require extensive and expensive preparation steps. While some aspects, such as photobleaching and the limited number of labels have been addressed, i.e. using photostable quantum dots,6 the requirement for labels and the costly and time consuming preparation steps, which are at the heart of a successful analysis,7 remain a significant challenge. To overcome these challenges some label-free HTS implementations based on reflectometric interference spectroscopy (RIfS),8 and surface plasmon resonance (SPR)9,10, have been explored.11,12 A big disadvantage is that those methods are primarily sensitive to ligand binding events, such as inhibition assays, and are not commonly used for a full content analysis

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

of single cells. There are, nevertheless, other spectroscopic methods, which have the potential to provide an intriguing alternative to fluorescent-based implementations. Raman spectroscopy has received significant attention in the recent years, because it provides biochemical information label-free with little to no sample preparation and has been widely used for the investigation of complex biological samples, such as eukaryotic and prokaryotic cells.13 The Raman Effect is based on an inelastic light scattering between a photon and a molecule, resulting in the excitation of a molecular vibration. The subsequent change in the photon energy is highly specific for the excited molecular bond vibration, and provides a distinct molecular fingerprint of the sample.14 Due to the intrinsic and label-free information of the sample, Raman spectroscopy bears significant promise to become a powerful tool in biomedical cell research.15 A Raman spectrum consists of many dozen independent parameters on the macromolecular makeup of a sample. In this context, the method has proven its potential in certain applications, ranging from the identification of eukaryotic and prokaryotic cells, characterization of drug-cell interaction, differentiation of apoptotic and necrotic cells, cell-cycle analysis, label-free lipodomics, and many others in proof-of-principle studies.16–21 Because of this unique and versatile applicability combined with an exceptional information depth, Raman spectroscopy is ideal for label-free high-throughput and high-content screening. A key obstacle for Raman spectroscopy in HTS applications is the complex data acquisition procedure, resulting in small data sizes, and frequently questionable statistical significance of the data.22 Despite the existing evidence that for a credible experimental design the sample size and throughput have to be on the order of hundreds or even many thousands of cells, the wide majority of publications rely on the analysis of a few dozen. The sample size, however, is especially crucial when dealing with mixed population samples where the estimation for the number of class-members is of importance, and the member ratios between the classes differ significantly, such as a cell phenotyping, or cell type classification.23 For example, Raman spectroscopy bears significant benefit for the analysis of leukocytes,24 and the detection of circulating tumor cells,25 because it provides biochemical information without the need for labels. It does therefore not impact additional down stream analysis, such as cell culturing of rare cells, mutation detection through rolling circle amplification, or other molecular biosensing in those cells. However, to establish a haemogram or to detect CTCs in a mixture of background cells it is paramount to measure large number of cells and to provide robust statistics. The acquisition time for individual Raman spectra of cells is on the order of several seconds26, or even minutes27–29 rendering high-throughput applications difficult and time consuming. But long acquisition times are not the only problem that makes high-throughput applications challenging, and publications with hundreds, or even thousands of cells are rarely found in the current literature.30 We have previously shown that Raman spectra from eukaryotic cells can be acquired in less than 1 s, using appropriate optical components, such as high-grade scientific charge coupled devices (CCD), reduced spectral resolution, and a combination with multivariate statistics.31–34 In this publication we introduce a high-throughput screening Raman spectroscopy platform, which provides several dozens macromolecular parameter for the rapid and label-free acquisition of Raman spectra from a large number of eukaryotic cells. By combining data acquisition strategy from HTS with Raman spectroscopy we have enabled new high-throughput applications with an acquisition of Raman spectra from more than 100.000 individual cells, and a fully automated sampling of 1000 single cells in less than 20 min. The modification in the acquisition strategy results in new applications, which are challenging to perform by standard Raman spectroscopy. We exemplify the new possibilities of the HTS-RS platform by two applications: 1. HTS-RS-based differential white blood cell count. 2. HTS-RS-based identification of circulating ACS Paragon Plus Environment

Page 2 of 15

Page 3 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


tumor cells in a mixture of leukocytes. Because a large number of possible applications with Raman spectroscopy have readily been shown, and the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research. Experimental section HTS-RS platform The custom-built upright Raman microscopy setup, see Figure S1a, is equipped with a 785 nm single-mode excitation laser (Xtra, Toptica, Germany), and a nominal output power of 400 mW. The excitation light is fiber-coupled, using a polarization-maintaining single-mode fiber, and coupled-out by fiber-port collimator (Thorlabs, Germany). A clean-up filter (785±1.5 nm; Semrock, USA) removes background contributions generated in the delivery fiber. The excitation light is reflected by a dichroic notch filter (785 nm, bandwidth 89 nm; Semrock, USA), and coupled into a 60x, water-immersion objective lens (NA =1.0; Nikon, Japan), which focuses the excitation laser beam into the sample plane. Because the beam underfills the objective lens, the beam diameter in the sample plane is expanded, covering an area of roughly 10 µm2. The sample rests on a custom-built sample holder mounted on two motorized translational stages (CONEX MFA-Series; Newport, USA). The motorized x–y translational stages are mounted on an automated z-positioning stage (MTS25-Z8, Thorlabs, USA), which allows an automated sample translation in the z-direction and focusing onto the sample plane. The generated Raman signal is collected by the same objective lens and passes an additional notch filter (785 nm ± 19 nm; Laser Components, Germany) used for a reliable suppression of the excitation light. An achromatic doublet (100 mm; Thorlabs, Germany) focuses the Raman signal onto a multimode fiber (105 µm core, Thorlabs, Germany). The fiber guides the light to a spectrometer (IsoPlane160, Princeton Instruments, USA) that is equipped with a grating with 400 grooves/mm, blazed at 750 nm, and allows a spectral resolution of 9 cm-1. The signal is dispersed onto a charge-coupled device (CCD) (PIXIS-400BR-eXcelon; Princeton Instruments, USA) with an active pixel-area of 400 × 1340 pixel, and a nominal quantum efficiency of up to 98 % at 800 nm. For the brightfield acquisition the sample is illuminated from below, using a standard white light LED and detected on CCD camera (DCC1645C, Thorlabs, Germany). The setup is controlled by in-house written data-acquisition software in LabView (National Instruments, USA) and is shown in Supplementary Material Fig. S1a. Spectral preprocessing and multivariate data analysis Raman spectra were analyzed in R, using the hyperSpec package.35 Preprocessing of spectra included intensity correction and wavelength calibration by recording spectra of a calibration lamp and 4-Acetaminophenol powder, respectively. Cosmic spike removal was performed using an cosmic spike removal algorithm developed by Ryabchykov et al.36 In order to correct for Raman signals of water and other unspecific scattering contributions in the Raman spectra Extended Multiplicative Scatter Correction (EMSC), as implemented in the cbmodels package, was used.37 Reference cell spectra with high signal-to-noise ratio were recorded separately and corrected by subtracting a water spectrum and a baseline estimated via an interpolating spline through support points, which were determined from a convex hull of the spectra. Background corrected Raman spectra with Pearson correlation coefficients below 0.95 regarding abovementioned reference spectra were discarded. Singular value decomposition of the respective ACS Paragon Plus Environment


spectral dataset and reconstruction with 20 components was applied to reduce noise level. Consecutively, Raman spectra were area-normalized relative to the whole spectral range of interest. Partial least squares – linear discriminant analysis (PLS-LDA) classification models were built in R combining the kernel PLS method in the PLSR-package38 and the LDA function in the MASS package39, respectively. A random forest model for classification was built via the „random Forest“ package40 provided in R. Briefly, the model was trained with 265 spectra each of leukocytes and Panc1 cells growing an ensemble of 500 decision trees with maximal number of nodes. The model-inherent bootstrapping of wavenumber subsets allows estimating the importance of specific wavenumber ranges for discrimination of both cell types. The out-of-bag error rate was found to be 3%.

Results and Discussion Conventional HTS-platforms are based on traditional fluorescence microscopes combined with an automated morphology analysis software.41–43 In contrast to fluorescence imaging, the acquisition of Raman images is highly time consuming, on the order of minutes to hours,44 and not suitable for a large-scale sampling of cells. Therefore, to realize the HTS-RS platform we have implemented the acquisition of single Raman spectra from individual cells in an automated fashion. We have developed a bright field cell recognition routine, based on a set of morphological operations on the bright field image, to determine the spatial location of cells in the sample plane, (Supplementary Material Fig. S1b). This automated cell localization was combined with an automated sampling of the individual cells, following the flowchart in (Supplementary Material Fig. S1c). The current implementation allows a sampling of more than 20.000 cells by Raman spectroscopy in less than 4 h. Details on the developed algorithm, the data acquisition strategy can be found in Supplementary Materials. HTS-RS-based differential white blood cell count HTS in automatic microscopy implementations are frequently assessing cell cultures with adherent cells. For blood cells, which are not adherent cells, the analysis is usually performed by flow cytometric high-throughput or high-content screening (FC-HTS or HCS).45,46 The informational content differs slightly, because while in microscopy the imaging information is assessed by image analysis, the morphological information in FC-HTS is based on light scattering from cells, providing information about size, granularity, nucleus, and others. Nevertheless, FC-HTS is frequently performed in combination with fluorescent labels, targeting the expression of certain proteins. Probably the most common clinical implementation of flow cytometry based measurement is the differential leukocyte count. It is a most frequently requested tests in clinics, because it is a significant indicator for a variety of diseases, such as immune disorder, infection and inflammation, and others.47 More modern and automated methods mostly rely on light scattering and on histochemical or antibody staining of the leukocytes. While these methods provide more specific information about disease, the results, specifically of histochemical staining, are not always easily interpretable, especially during disease state where cell surface marker change drastically and the results of tests are highly dependent on the reagents and conditions used, such as pH of the staining solution.47 Ramoji et al. have shown that Raman imaging spectroscopy is quite suitable in the identification of leukocyte classes, and provides comparable morphological information of leukocytes as conventional methods, however, label-free.24 In the same publication the researchers have shown that CD4+ T-lymphocytes cannot only be morphologically differentiated from other leukocytes, but indeed have a distinctly different molecular makeup, which can be assessed by Raman ACS Paragon Plus Environment

Page 4 of 15

Page 5 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


spectroscopy. The sample size in that study, i.e. total number of cells investigated, was 47, and measured on the individual cell classes separately. Chan et al. have used Raman spectroscopy for the differentiation between neoplastic and normal lymphocytes, with a total of 183 sampled cells.48 There are several other promising publications, which outline the potential of Raman spectroscopy for the characterization of leukocytes.49–51 As such, Raman spectroscopy can provide a complimentary alternative to readily established methods, because it allows assessing intrinsic biomolecular differences in cells without additional preparation steps, and increases the analytical capability for a given sample. Reliable characterization and quantification of blood cells is currently in high demand in pathology. As such a label-free method, which can perform cell identification and simultaneously provides a cellular activation profile, will revolutionize hematology. To show that Raman spectroscopy can indeed perform a label-free biomolecularbased leukocyte differential count, we have used the HTS-RS platform to analyze white blood samples from 6 volunteers and 3 patients. We have focused on three major leukocyte types, which together account for more than 97 % of the white blood cell population, i.e. neutrophils, lymphocytes, and monocytes. Each of these cells can get modified in different ways, depending on the disease. The three cell types were isolated from four healthy volunteers to establish a model for the identification of cell types in mixed populations. Neutrophils and lymphocytes were extracted for all four volunteers, and the monocytes were extracted for the first two volunteers. The extracted cells were fixed and stored at 4°C. For the measurement the cells were pipetted on a poly-L-lysine coated CaF2 substrate to prevent them from moving on the substrate during the measurements. The number of sampled cells for each cell type and patient are indicated in Table 1. The total number of acquired Raman spectra for the four volunteers was 107.738 with a maximum acquisition speed of up to 20.000 cells in less than 4 h. Table 1 Raman spectra were acquired of extracted neutrophils, lymphocytes, and monocytes from 4 healthy volunteers. The number of samples per cell type and patient is indicated. The total number of sampled cells to build up the multivariate statistical model is 107.738.

Cell type

Volunteer 1

Volunteer 2

Volunteer 3

Volunteer 4

Total

Neutrophils

4.657

24.894

5.660

13.009

48.220

Lymphocytes

10.405

12.587

5.773

23.453

52.218

Monocytes

6.323

971

n.d.

n.d.

7.294

Total

21.385

38.452

11.433

36.468

107.738

An example for the distribution of leukocytes on the CaF2 substrate, which were measured by Raman spectroscopy, is shown in (Supplementary Material Fig. S2). In this example, cells distributed over an area of 6.75 x 5.16 mm² were sampled in 4 h 32 min. A single FOV is 150x120 µm², and the entire image consists of 45x43 individual FOVs. The total number of cells sampled over the entire region is 21.859. To provide a better view on the measured cells distributed on the substrate indicated regions with different sizes were magnified and are displayed next to the large tile. The cells are shown in black with a white edge on a red background. Following the HTS-RS data acquisition, all spectra were processed identically, e.g. remove cosmic spikes, and background correction, see paragraph ‘Spectral preprocessing and ACS Paragon Plus Environment


multivariate data analysis’ in Experimental section. The mean spectra and the standard deviation for the three leukocyte types, containing all measured spectra for that class, and are plotted in Fig. 1a. For a better visualization the intensity of high wavenumber region was downscaled 2fold. All cell spectra were used to build a PLS-LDA model. The projection of all cell spectra into the space spanned by LD 1 and 2 is shown as a two-dimensional histogram in Fig. 1b. The shade of each hexagon in the plot corresponds to the indicated number of cells. The histogram plot readily shows that the three classes have a distinct separation, based on their molecular fingerprint. Especially neutrophils create a distinct cluster, due to a reduced nucleic acid content in comparison to lymphocytes. Raman spectra of both lymphocytes and monocytes contain noticeable contributions of carotenoids as an additional discriminatory feature. While lymphocytes also display a distinct cluster, the monocytes are more spread and overlap to some degree with lymphocytes, which is expected because lymphocytes and monocytes together fall into peripheral blood mononuclear cells (PBMCs) group. White points and colored lines indicate the median of each class as well as the corresponding 50th percentile, respectively. The information on the underlying biochemical separation between the three groups is explained by the LD coefficients, Fig. 1c. Moreover, a summary about the high-content molecular information with all relevant peak assignments of the molecular fingerprint of the cells can be found in supplemental figure (S3). After successfully establishing a PLS-LDA model for the differentiation of neutrophils, lymphocytes, and monocytes Raman spectra were acquired from mixed leukocyte populations of 2 healthy volunteers and 3 patients. The data acquisition and spectral processing was identical to the previous experiments on the three extracted leukocytes classes. Cell type classification was performed, using the PLS-LDA model trained with the Raman spectra of isolated leukocytes. For comparison between Raman spectroscopy and standard method the same blood samples were also processed using machine counting in a clinical setting. Results of both blood count modalities are summarized for comparison in Table 2. Raman measurements provide comparable results to machine counting. While there are deviations for some measurements, the overall results are in good agreement. The three recruited patients were all hospitalized and were confirmed with the presence of an infection. Furthermore, as a result of severe infection patient 1 and 3 displayed a very low lymphocyte count. In such cases neutrophils and monocytes are highly active, resulting in more cells being recognized as monocytes, see patient 3. To our knowledge this is the first time that Raman spectroscopy has been performed with such complex samples and such a large number of cells. It has to be underlined that the standard measurements were performed on whole blood, while for the Raman measurements the erythrocytes were removed from the sample, which can also add to some of the observed deviations. Besides, the machine-based counting has an inherent error as well. The accuracy for the Sysmex cell counter is ~95% and does not provide activation profile of the cell unless specifically stained for. The observed deviations for all recovered subtypes, i.e. neutrophils, lymphocytes, and monocytes are well within the total allowable error (TAE), which is given with 23.4%, 14.5%, and 15%, as summarized by Buttarello and Plebani, for the analytical goal of a differential WBC count.52 To show that the acquisition of Raman spectra from a low number of cells would have negative effects on the prediction and the statistical reliability of the results we have performed a random drawing of a fixed number of cell spectra from one of the test data sets, and calculated the estimated leukocyte differential count, Fig. 1d. This was done for 10 random drawings. For example, when using only 100 cells from the data set the estimated value for the relative number of cells underlies significant fluctuations for the estimation of the leukocyte differential count, resulting in unreliable values. These deviations will decrease with an increasing number of ACS Paragon Plus Environment

Page 6 of 15

Page 7 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


sampled cells, but even with 500 sampled cells the deviation from the mean is still significant. Only at 1000 cells the deviation begins to approach the stable mean established by the model. This number of cells can be acquired in ca. 15 min. With further modifications on the setup, e.g. changing to a 532 nm excitation source and more efficient computing the acquisition time can be further reduced to less than 5 min. While this is still comparably slow to flow cytometry the device can be used for other cell experiment types without any requirement for staining or other preparation steps. The HTS-RS measurements highlight the importance to sample a large number of cells to establish statistically relevant information from a mixed population of cells. While for some applications it is required to establish a training set for classification, it is only required once. The training set can be used for any further analysis, as shown with the experiments on the mixed populations. The advantage is that the data can also be transferred between comparable devices, which enhance the capability of the method. For future application this approach will reduce the cell-to-cell variability and provide more disease relevant alterations within cells. Hence, it opens a broad field of application in the study of leukocytes, the changes in absolute quantity of individual population and their role during infection, autoimmune diseases, shock injury, etc.

Figure 1. (a) Mean and standard deviation of normalized spectra for the three cell classes acquired from the extracted leukocyte classes. The high wavenumber region was reduced in intensity 2-fold. (b) Cell spectra projected onto the space spanned by LD 1 and 2. Three distinct groups can be seen. The median of each group is shown by the white dot and the line indicates the 50th percentile. The LD 1 and 2 scattering plot of the model shows measurements of a total of 107.738 cells. (c) The corresponding LDA-coefficients for LD 1 and 2, which explain the spectral features responsible for the separation shown in the scatter plot. (d) Random drawing of different sample sizes shows



Page 8 of 15

that a low number of measurements will result in a high deviation from the real leukocyte differential count determined by the model. Table 2 Comparison of differential leukocyte count for 2 healthy volunteers and 3 patients based on Raman measurements in a mixed population and clinical evaluation by machine counting. R – Raman-based; C – Machinebased

Volunteer 5 Volunteer 6

Patient 1

Patient 2

Patient 3

Cell type

R in %

C in %

R in %

C in %

R in %

C in %

R in %

C in %

R in %

C in %

Neutrophils

66

68

82

72

82

79

69

62

69

81

Lymphocytes

21

23

13

11

6

13

20

26

9

8

Monocytes

13

8

5

15

12

9

10

9

22

8

HTS-RS-based identification of CTCs in a mixture of leukocytes

Figure 2a. Example from a single FOV, where one pancreatic cancer cell was identified in between 12 leukocytes. Raman spectra for two leukocytes and the pancreatic cancer cell are plotted to show the spectral differences. (b) Mean spectra and standard deviation for pancreatic cancer cells and leukocytes. The bar below indicates the most discriminatory features from the spectrum, with red indicating spectral locations, which contribute most to the separation between the cell types based on Random Forest.

Circulating tumor cells are primary tumor cells that have been shed into the vascular system, potentially responsible for the development of tumor metastasis.53 An accurate identification of CTCs is very challenging, because there are frequently less than 100 CTCs present in 1 ml of whole blood. On the other hand, it has been widely shown that a precise detection of CTCs provides many prognostic and diagnostic opportunities and could replace the highly invasive tissue biopsies through liquid biopsies.54,55 Most commonly a CTC count is performed in a multistep process, and involves an enumeration and a detection step. It is generally accepted that biochemical and physical strategies have to be combined in an appropriate way to enumerate and to detect and CTCs.56,57 Biochemical detection approaches, such as CellSearch system58 and the GILUPI wire59, use EpCAM antibodies to target tumor-cell specific antigens, in possible combined with CD45 to deplete leukocytes with immunomagnetic beads. However, the CTC yield of CellSearch is low and the system is only approved for breast, colorectal and prostate ACS Paragon Plus Environment

Page 9 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


cancer. The low yield is due to the loss of cells during sample processing, but also because antigens are not present on all CTC, due to an ependymal-to-mesenchymal transition. Physical methods utilize the size and morphology to enrich CTCs and are based on dielectrophoresis (DEPArray)60, immunomagnetic separation in a microfluidic device (IsoFlux)61, and inertial focusing-enhanced microfluidics (CTC-iChip)62. Although, these methods are label-free, they lack specificity and following the enrichment step other methods are needed to identify CTCs, ideally also in a label-free way. Raman spectroscopy is a promising candidate for a full label-free CTCs enumeration and has been shown in model systems with cultured cells.30,25 The limited sensitivity of Raman spectroscopy in clinical settings can be overcome with 100-fold enrichment, e.g. 10,000 cells instead of 1,000,000 cells, in combination with high throughput screening. Besides, knowing the purity of the cells after the extraction can be of significant advantage for cell cultivation. This, however, is challenging if cells have to be a priori stained in the detection step. Because Raman spectroscopy does not require labels and is non-destructive, it does not affect the integrity of cells, which is highly important for subsequent biochemical assays, such as polymerase chain reaction, rolling circle amplification or next generation sequencing. There is a large number of publications which have shown that Raman spectroscopy can correctly classify different types of cancer cells63,64 and bears potential for the identification of circulating tumor cells.65–67 However, experiments are usually performed separately on individual cell types and evaluated using exhaustive and non-exhaustive cross-validation approaches, such as leave-n-out or k-fold cross-validation.68 While cross-validation approaches can be quite suitable when correctly performed, i.e. large number of cells and independent batches,22 there is, nonetheless, room for systematic errors, which can result in differentiation between classes not based on real spectral difference. Here we wanted to test if the newly developed HTS-RS platform can also label-free differentiate between pancreatic cancer cells and leukocytes, as they commonly occur after the enumeration step. We have prepared mixtures of EpCAM positive pancreatic cancer cells Panc-1, and leukocytes, as outline in Supplementary Materials, in three different ratios 50/50, 1/10, and 1/100, see Table 3. Cancer cell / leukocyte mixtures were pipetted on the CaF2 coverslip and Raman measurements were performed in an automated fashion with the developed data acquisition method. The acquired data was processed, and classified using Random Forest classification. An example from a single FOV is shown in Fig. 2a where several cells are randomly distributed on the substrate. The red dots in the image indicate the location where a HTS-RS measurement was performed. Raman spectra for three measured cells are also plotted. Two of the indicated cells were identified by Random Forest classification as leukocytes, the other cell as a pancreatic cancer cell. The spectral differences are primarily due to higher relative protein content and reduced lipid content in leukocytes, as in comparison to the pancreatic cancer cells. We have previously already shown similar spectral differences between leukocytes and pancreatic cancer cells.30,34 The mean Raman spectra for leukocytes and the pancreatic cancer cell are plotted in Fig. 2b. The standard deviation of the spectra shows that the Raman spectra of the pancreatic cancer cells are more homogeneous, while the Raman spectra of the leukocytes are more heterogeneous. This is due to the presence of multiple types of leukocytes in the sample, each owing a specific Raman signature, as was readily shown in the leukocyte cell classification section. Besides a cell classification Random Forest also provides information about spectral features, which highly contribute to the separation. These features are plotted in the bar graph below the mean Raman spectra in Fig. 2b. Red indicates spectral regions highly contributing to the separation, while blue indicates a lower contribution to the separation. The key features in the low wavenumber region are the bands at 1379 cm-1 and 1571 cm-1 corresponding to DNA-RNA ring-breathing modes of (T, A, G) and (G, A), respectively.69 The main difference in the highACS Paragon Plus Environment


wavenumber region is the higher presence of lipid content from the CH2 stretching vibration at 2854 cm-1, in the pancreatic cancer cells in comparison to the leukocytes. A more detailed differentiation between the differences in molecular content between the pancreatic cancer cells and the leukocytes can be found in Supplementary Materials S3. The comparison between the prepared ratios of cell mixtures and the ratios of mixtures established by the Raman measurements are shown in Table 3. The established mixture and the HTS-RS measured values are strikingly similar. Even for the lower mixture, i.e. 1/100 the prediction based on the HTS-RS measurements provides highly accurate results, and the sampling of nearly 1000 cells could be achieved in 38 min. The total sampling time can be further reduced if the cell density on the coverslip is increased and a 532 nm excitation source is used. It remains to be seen how large an error for the CTC detection can be to provide conclusive trends in a clinical setting, because as of now, different methods for CTC detection deviate significantly, and it has been reported that the only FDA-approved method, i.e. CellSearch, might underestimate the number of CTCs between 30-100 fold. 70 Table 3 Comparison between the prepared mixture of leukocytes and pancreatic cancer cells and ratios established by Raman spectroscopy in these mixtures. The ratios determined by Raman nicely resemble the prepared mixed ratios.

Prepared Mixtures Panc-1/ leukocytes

Established by HTS-RS Panc-1/ leukocytes

0.5 / 0.5

0.55 / 0.45

0.1 / 0.9

0.1 / 0.9

0.01 / 0.99

0.03 / 0.97

Conclusion In this manuscript we have presented an implementation of high-throughput screening Raman spectroscopy (HTS-RS) platform for label-free cellomics. To achieve high analytical potential, conventional HTS platforms rely on complex fluorescence staining of cellular compartments and proteins of interest, significantly increasing the complexity of the approach. To overcome the complex sample preparation steps and to increase the analytical capacity of HTS we propose a label-free HTS-RS approach, which allows measurements of the intrinsic molecular makeup of eukaryotic cells, providing a higher number of parameters, when compared with fluorescencebased HTS, but without the requirement for time-expensive and costly preparation procedures. Because no sample preparation is required, cells can simply be placed on the sample carrier and measured. The capability of HTS-RS has been shown on two applications that have previously not been possible by conventional Raman spectroscopy acquisition approaches. The HTS-RSbased differential white blood cell count experiments showed the possibility to categorize leukocytes into their respective lineage and obtained numbers comparable to machine counting. The trend of relative ratio of neutrophils to PBMCs has been correctly predicted. The HTS-RSbased differential white blood cell count enables new ways to detect infections, using absolute cell population counts and label-free phenotyping of cells. The application platform of HTS-RSbased differential count can be further extended to identify immature and abnormal cells and categorize patients into disease types using the biochemical information provided by Raman spectra. The HTS-RS-based identification of circulating tumor cells in a mixture with leukocytes ACS Paragon Plus Environment

Page 10 of 15

Page 11 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


showed that Raman spectroscopy can determine pancreatic cancer cells in a mixture of leukocytes and estimate the actual ratios of the mixtures. Ultimately, both white blood cell differential and CTC detection can be done simultaneously very rapidly, ca. 38 min., leaving space for monitoring other related key health parameters. Because Raman spectroscopy has been shown to be successfully applied to a very large number of biomedical problems, ranging from drug-cell interaction, to stem-cell identification, to label-free lipodomics the proposed HTS-RS platform opens-up many new and exciting applications for Raman spectroscopy and paves the way for Raman spectroscopy to become a versatile tool in cell biology and clinical applications. Acknowledgement Financial support of the EU-Funded project CanDo (FP7 ICT 610472) and the project “HemoSpec” (FP 7, CN 611682). DFG via the project diatoms (FKZ KR4387/1-1), BMBF via the integrated research and treatment center, and the “Center for Sepsis Control and Care” (FKZ 01EO1502) are highly acknowledged.

Author contribution IWS, AR, UN, CK and JP designed experiments; IWS, JR, SAM performed experiments; IWS and SAM wrote algorithm for device control and image recognition; JR performed data analysis; AR and UT extracted and prepared cells for leucocyte experiments, performed clinical evaluation; JR prepared CTC mixtures; IWS, JR, CK, and JP prepared the manuscript

Supporting Information Supplementary Materials and Methods, description of cell detection algorithm, band assignments for all Raman spectra of eukaryotic cells

References (1) Beckerman, S. R.; Jimenez, J. E.; Shi, Y.; Al-Ali, H.; Bixby, J. L.; Lemmon, V. P. Assay Drug Dev. Technol. 2015, 13, 377–388. (2) Nieland, T. J. F.; Logan, D. J.; Saulnier, J.; Lam, D.; Johnson, C.; Root, D. E.; Carpenter, A. E.; Sabatini, B. L. PLoS One 2014, 9, 1–11. (3) Boutros, M.; Heigwer, F.; Laufer, C. Cell 2015, 163, 1314–1325. (4) Abraham, V. C.; Taylor, D. L.; Haskins, J. R. Trends in Biotechnology. 2004, pp 15–22. (5) Usaj, M. M.; Styles, E. B.; Verster, A. J.; Friesen, H.; Boone, C.; Andrews, B. J. Trends Cell Biol. 2016, 26, 598–611. (6) Zhang, T.; Stilwell, J. L.; Gerion, D.; Ding, L.; Elboudwarej, O.; Cooke, P. A.; Gray, J. W.; Alivisatos, A. P.; Chen, F. F. Nano Lett. 2006, 6, 800–808. (7) Giuliano, K. A.; Haskins, J. R.; Taylor, D. L. Assay Drug Dev. Technol. 2003, 1, 565–577. (8) Sauer, M.; Brecht, A.; Charissé, K.; Maier, M.; Gerster, M.; Stemmler, I.; Gauglitz, G.; Bayer, E. Anal. Chem. 1999, 71, 2850–2857. (9) Maynard, J. A.; Lindquist, N. C.; Sutherland, J. N.; Lesuffleur, A.; Warrington, A. E.; Rodriguez, M.; Oh, S. H. Biotechnology Journal. WILEY VCH Verlag November 2009, pp 1542–1558. (10) Piliarik, M.; Vaisocherová, H.; Homola, J. Biosens. Bioelectron. 2005, 20, 2104–2110. (11) Proll, G.; Steinle, L.; Pröll, F.; Kumpf, M.; Moehrle, B.; Mehlmann, M.; Gauglitz, G. J. Chromatogr. A 2007, 1161, 2–8. (12) Mayr, L. M.; Bojanic, D. Curr. Opin. Pharmacol. 2009, 9, 580–588. (13) Krafft, C.; Schmitt, M.; Schie, I. W.; Cialla-May, D.; Matthäus, C.; Bocklitz, T.; Popp, J. Angewandte Chemie - International Edition. 2017, pp 4392–4430. (14) Smith, Z. J.; Huser, T. R.; Wachsmann-Hogiu, S. Anal. Cell. Pathol. 2012, 35, 145–163. ACS Paragon Plus Environment


(15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34) (35) (36) (37) (38) (39) (40)

Carey, P. R. Journal of Biological Chemistry. 1999, pp 26625–26628. Pahlow, S.; Meisel, S.; Cialla-May, D.; Weber, K.; Rosch, P.; Popp, J. Adv. Drug Deliv. Rev. 2015, 89, 105–120. Mazur, A. I.; Monahan, J. L.; Miljković, M.; Laver, N.; Diem, M.; Bird, B. J. Biophotonics 2013, 6, 101–109. Kann, B.; Offerhaus, H. L.; Windbergs, M.; Otto, C. Adv. Drug Deliv. Rev. 2015. Konorov, S. O.; Schulze, H. G.; Piret, J. M.; Blades, M. W.; Turner, R. F. B. Anal. Chem. 2013, 85, 8996–9002. Wu, H.; Volponi, J. V.; Oliver, A. E.; Parikh, A. N.; Simmons, B. A.; Singh, S. Proc. Natl. Acad. Sci. 2011, 108, 3809–3814. Schie, I. W.; Huser, T. Appl. Spectrosc. 2013, 67, 813–828. Beleites, C.; Neugebauer, U.; Bocklitz, T.; Krafft, C.; Popp, J. Anal. Chim. Acta 2013, 760, 25–33. Lenth, R. V. Am. Stat. 2008, 55, 187–193. Ramoji, A.; Neugebauer, U.; Bocklitz, T.; Foerster, M.; Kiehntopf, M.; Bauer, M.; Popp, J. Anal. Chem. 2012, 84, 5335–5342. Neugebauer, U.; Bocklitz, T.; Clement, J. H.; Krafft, C.; Popp, J. Analyst 2010, 135, 3178. Dochow, S.; Krafft, C.; Neugebauer, U.; Bocklitz, T.; Henkel, T.; Mayer, G.; Albert, J.; Popp, J. Lab Chip 2011, 11, 1484–1490. Puppels, G. J.; de Mul, F. F.; Otto, C.; Greve, J.; Robert-Nicoud, M.; Arndt-Jovin, D. J.; Jovin, T. M. Nature 1990, 347, 301–303. Gniadecka, M.; Wulf, H. C.; Nymark Mortensen, N.; Faurskov Nielsen, O.; Christensen, D. H. J. Raman Spectrosc. 1997, 28, 125–129. Chan, J. W.; Taylor, D. S.; Lane, S. M.; Zwerdling, T.; Tuscano, J.; Huser, T. Anal. Chem. 2008, 80, 2180–2187. Schie, I. W.; Kiselev, R.; Krafft, C.; Popp, J. Analyst 2016, 141, 6387–6395. Krafft, C.; Schie, I. W.; Meyer, T.; Popp, J. Chem. Soc. Rev. 2015. Kiselev, R.; Schie, I. W.; Aškrabić, S.; Krafft, C.; Popp, J. Biomed. Spectrosc. Imaging 2016, 5, 115–127. Schie, I. W.; Alber, L.; Gryshuk, A. L.; Chan, J. W. Analyst 2014, 139, 2726–2733. Schie, I. W.; Krafft, C.; Popp, J. Journal of Biophotonics. 2016, pp 994–1000. Beleites, C.; Sergo, V. hyperSpec a Packag. to handle hyperspectral data sets R. URL http//hyperspec.r-forge.r-project.org, 2012. Ryabchykov, O.; Bocklitz, T.; Ramoji, A.; Neugebauer, U.; Foerster, M.; Kroegel, C.; Bauer, M.; Kiehntopf, M.; Popp, J. Chemom. Intell. Lab. Syst. 2016, 155, 1–6. C. Beleites. 2015. Mevik, B.-H.; Wehrens, R.; Liland, K. H. 2013. Venables, W. N.; Ripley, B. D. In Modern applied statistics with S; 2002; pp 271–300. Bento, A. P.; Gaulton, A.; Hersey, A.; Bellis, L. J.; Chambers, J.; Davies, M.; Kruger, F. A.; Light, Y.; Mak, L.; McGlinchey, S.; Nowotka, M.; Papadatos, G.; Santos, R.; Overington, J. P.; Bredel, M.; Jacoby, E.; Breiman, L.; Fourches, D.; Muratov, E.; Tropsha, A.; Freyhult, E.; Prusis, P.; Lapinsh, M.; Wikberg, J. E.; Moulton, V.; Gustafsson, M. G.; Gaulton, A.; Bellis, L. J.; Bento, A. P.; Chambers, J.; Davies, M.; Hersey, A.; Light, Y.; McGlinchey, S.; Michalovich, D.; Al-Lazikani, B.; Overington, J. P.; Kooistra, A. J.; Kuhne, S.; de Esch, I. J. P.; Leurs, R.; de Graaf, C.; Paricharak, S.; Cortés-Ciriano, I.; IJzerman, A. P.; Malliavin, T. E.; Bender, A.; Rezácová, P.; Borek, D.; Moy, S. F.; Joachimiak, A.; Otwinowski, Z.; Strobl, C.; Boulesteix, A.-L.; Zeileis, A.; Hothorn, T.; Van Westen, G. J. P.; Van Den Hoven, O. O.; Van Der Pijl, R.; MulderACS Paragon Plus Environment

Page 12 of 15

Page 13 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(41)

(42) (43) (44) (45) (46) (47) (48) (49) (50) (51) (52) (53)

(54) (55)

(56) (57) (58) (59)

(60) (61)

(62)

Krieger, T.; De Vries, H.; Wegner, J. K.; IJzerman, A. P.; van Vlijmen, H. W. T.; Bender, A.; Swier, R. F.; Wegner, J. K.; IJzerman, A. P.; van Vlijmen, H. W. T.; Bender, A.; Liaw, A.; Wiener, M.; Jorgensen, W. L.; Friedman, J. H.; Van Westen, G. J. P.; Wegner, J. K.; Geluykens, P.; Kwanten, L.; Vereycken, I.; Peeters, A.; IJzerman, A. P.; van Vlijmen, H. W. T.; Bender, A.; Breitenbach, M.; Nielsen, R.; Grudic, G. Z.; Meinshausen, N.; Hatko, S. Nucleic Acids Res. 2013, 5, 983–999. Carpenter, A. E.; Jones, T. R.; Lamprecht, M. R.; Clarke, C.; Kang, I. H.; Friman, O.; Guertin, D. a; Chang, J. H.; Lindquist, R. a; Moffat, J.; Golland, P.; Sabatini, D. M. Genome Biol. 2006, 7, R100. Baatz, M.; Arini, N.; Schäpe, A.; Binnig, G.; Linssen, B. Cytom. Part A 2006, 69, 652– 658. Lee, S.; Howell, B. J. Methods in Enzymology. 2006, pp 468–483. Miljković, M.; Chernenko, T.; Romeo, M. J.; Bird, B.; Matthäus, C.; Diem, M. Analyst 2010, 135, 2002–2013. Brinkman, R. R.; Gasparetto, M.; Lee, S.-J. J.; Ribickas, A. J.; Perkins, J.; Janssen, W.; Smiley, R.; Smith, C. Biol. Blood Marrow Transplant. 2007, 13, 691–700. Naumann, U.; Wand, M. P. Cytom. Part A 2009, 75A, 789–797. Blumenreich, M. Clin. Methods Hist. Phys. Lab. Exam. 3rd Ed. 1990, 724–727. Chan, J. W.; Taylor, D. S.; Zwerdling, T.; Lane, S. M.; Ihara, K.; Huser, T. Biophys. J. 2006, 90, 648–656. Mannie, M. D.; McConnell, T. J.; Xie, C.; Li, Y.-Q. J. Immunol. Methods 2005, 297, 53– 60. Harz, M.; Kiehntopf, M.; Stöckel, S.; Rösch, P.; Deufel, T.; Popp, J. Analyst 2008, 133, 1416. Neugebauer, U.; Clement, J. H.; Bocklitz, T.; Krafft, C.; Popp, J. J Biophotonics 2010, 3, 579–587. Buttarello, M.; Plebani, M. Am. J. Clin. Pathol. 2008, 130, 104–116. Cristofanilli, M.; Budd, G. T.; Ellis, M. J.; Stopeck, A.; Matera, J.; Miller, M. C.; Reuben, J. M.; Doyle, G. V.; Allard, W. J.; Terstappen, L. W. M. M.; Hayes, D. F. N. Engl. J. Med. 2004, 351, 781–791. de Bono, J. S. Clin. Cancer Res. 2008, 14, 6302–6309. Cohen, S. J.; Punt, C. J. A.; Iannotti, N.; Saidman, B. H.; Sabbath, K. D.; Gabrail, N. Y.; Picus, J.; Morse, M.; Mitchell, E.; Miller, M. C.; Doyle, G. V; Tissing, H.; Terstappen, L. W. M. M.; Meropol, N. J. J. Clin. Oncol. 2008, 26, 3213–3221. Yu, M.; Stott, S.; Toner, M.; Maheswaran, S.; Haber, D. A. J. Cell Biol. 2011, 192, 373– 382. Alix-Panabières, C.; Pantel, K. Lab Chip 2014, 14, 57–62. Miller, M. C.; Doyle, G. V.; Terstappen, L. W. M. M. J. Oncol. 2010, 2010, 1–8. Saucedo-Zeni, N.; Mewes, S.; Niestroj, R.; Gasiorowski, L.; Murawa, D.; Nowaczyk, P.; Tomasi, T.; Weber, E.; Dworacki, G.; Morgenthaler, N. G.; Jansen, H.; Propping, C.; Sterzynska, K.; Dyszkiewicz, W.; Zabel, M.; Kiechle, M.; Reuning, U.; Schmitt, M.; Lücke, K. Int. J. Oncol. 2012, 41, 1241–1250. Harb, W.; Fan, A.; Tran, T.; Danila, D. C.; Keys, D.; Schwartz, M.; Ionescu-Zanetti, C. Transl. Oncol. 2013, 6, 528–IN1. Alva, A.; Friedlander, T.; Clark, M.; Huebner, T.; Daignault, S.; Hussain, M.; Lee, C.; Hafez, K.; Hollenbeck, B.; Weizer, A.; Premasekharan, G.; Tran, T.; Fu, C.; IonescuZanetti, C.; Schwartz, M.; Fan, A.; Paris, P. J. Urol. 2015, 194, 790–798. Ozkumur, E.; Shah, A. M.; Ciciliano, J. C.; Emmink, B. L.; Miyamoto, D. T.; Brachtel, E.; ACS Paragon Plus Environment


(63) (64) (65) (66) (67) (68) (69) (70)

Yu, M.; Chen, P. -i.; Morgan, B.; Trautwein, J.; Kimura, A.; Sengupta, S.; Stott, S. L.; Karabacak, N. M.; Barber, T. A.; Walsh, J. R.; Smith, K.; Spuhler, P. S.; Sullivan, J. P.; Lee, R. J.; Ting, D. T.; Luo, X.; Shaw, A. T.; Bardia, A.; Sequist, L. V.; Louis, D. N.; Maheswaran, S.; Kapur, R.; Haber, D. A.; Toner, M. Sci. Transl. Med. 2013, 5, 179ra47179ra47. Chen, K.; Qin, Y.; Zheng, F.; Sun, M.; Shi, D. Opt. Lett. 2006, 31, 2015–2017. Draux, F.; Jeannesson, P.; Beljebbar, A.; Tfayli, A.; Fourre, N.; Manfait, M.; Sulé-Suso, J.; Sockalingum, G. D. Analyst 2009, 134, 542–548. Neugebauer, U.; Bocklitz, T.; Clement, J. H.; Krafft, C.; Popp, J. Analyst 2010, 135, 3178– 3182. Ranc, V.; Srovnal, J.; Kvítek, L.; Hajduch, M. Analyst 2013, 138, 5983. Kong, K.; Kendall, C.; Stone, N.; Notingher, I. Advanced Drug Delivery Reviews. 2015, pp 121–134. Kohavi, R. Ijcai 2016. Bankapur, A.; Zachariah, E.; Chidangil, S.; Valiathan, M.; Mathur, D. PLoS One 2010, 5, e10427. Hong, B.; Zu, Y. Theranostics. 2013, pp 377–394.


Page 14 of 15

Page 15 of 15 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


for TOC only


High-Throughput Screening Raman Spectroscopy Platform for Label

Recommend Documents