The characterization of circulating tumor cells (CTCs) is a popular topic in translational research. This protocol describes a semi-automatic immunofluorescence (IF) assay for PD-L1 characterization and enumeration of CTCs in non-small cell lung cancer (NSCLC) patient samples.
Circulating tumor cells (CTCs) derived from the primary tumor are shed into the bloodstream or lymphatic system. These rare cells (1−10 cells per mL of blood) warrant a poor prognosis and are correlated with shorter overall survival in several cancers (e.g., breast, prostate and colorectal). Currently, the anti-EpCAM-coated magnetic bead-based CTC capturing system is the gold standard test approved by the U.S. Food and Drug Administration (FDA) for enumerating CTCs in the bloodstream. This test is based on the use of magnetic beads coated with anti-EpCAM markers, which specifically target epithelial cancer cells. Many studies have illustrated that EpCAM is not the optimal marker for CTC detection. Indeed, CTCs are a heterogeneous subpopulation of cancer cells and are able to undergo an epithelial-to-mesenchymal transition (EMT) associated with metastatic proliferation and invasion. These CTCs are able to reduce the expression of cell surface epithelial marker EpCAM, while increasing mesenchymal markers such as vimentin. To address this technical hurdle, other isolation methods based on physical properties of CTCs have been developed. Microfluidic technologies enable a label-free approach to CTC enrichment from whole blood samples. The spiral microfluidic technology uses the inertial and Dean drag forces with continuous flow in curved channels generated within a spiral microfluidic chip. The cells are separated based on the differences in size and plasticity between normal blood cells and tumoral cells. This protocol details the different steps to characterize the programmed death-ligand 1 (PD-L1) expression of CTCs, combining a spiral microfluidic device with customizable immunofluorescence (IF) marker set.
Tumor antigen-specific cytotoxic T-lymphocytes (CTLs) play a crucial role in the response to cancers through a process known as cancer "immune surveillance". Their anti-tumor functions are enhanced by immune checkpoint blockade antibodies such as CTLA-4 inhibitors and PD-1/PD-L1 inhibitors. In non-small cell lung cancer (NSCLC), anti-PD-1/PD-L1 therapies result in response rates ranging from 0%-17% in patients with PD-L1-negative tumors and 36%-100% in those expressing PD-L1. The robust responses to PD-1/PD-L1 blockade observed in melanoma and NSCLC are shown by evidence of improved overall response rate (RR), durable clinical benefits, and progression-free survival (PFS). Currently, anti-PD1 treatments are the standard of care in second-line NSCLC treatment with nivolumab regardless of PD-L1 expression and with pembrolizumab in patients expressing PD-L1 ≥1%. In first-line treatment, standard of care is pembrolizumab alone in patients with NSCLC expressing PD-L1 ≥50% and can be potentially enhanced with chemotherapy (platin and doublet drug depending on histologic subtype)1,2.
However, such an approach to patient management is debatable3, since PD-L1 expression in tumor cells by immunohistochemistry (IHC) is probably not the most ideal companion biomarker. Others such as tumor mutation burden4 (TMB), microsatellite instability (MSI), and/or microbiota are possibly interesting in this setting either alone or in combination. NSCLC are known to be heterogeneous tumors, either spatially (from a tumor site to another one) or temporally (from diagnosis to recurrence). Patients with NSCLC are usually fragile, and iterative invasive tissue biopsies may be an issue. Indeed, re-biopsy rate at first progression ranges from 46%-84% depending on series, and successful re-biopsy (meaning with histological and full molecular analysis) ranges from 33%-75%. This means that 25%-67% of patients cannot receive a comprehensive re-biopsy analysis during first progression5,6,7,8.
The advent of "liquid biopsies" has thus generated considerable enthusiasm in this particular setting, as it enables crucial reassessment of molecular alterations during disease progression by examining circulating free DNA (cfDNA) derived from circulating tumor cells (CTCs). These live cells are released from the tumor into the bloodstream, where they circulate freely. Although not routinely used, the analysis of CTCs appears to be highly promising in the case of molecular and phenotypic characterization, prognosis, and predictive significance in lung cancer (via DNAseq, RNAseq, miRNA and protein analysis). Indeed, CTCs likely harbor phenotypic characteristics of the active disease rather than the initial markers (detected on tissue biopsies at diagnosis). Furthermore, CTCs bypass the problem of spatial heterogeneity of the tumor tissue, which may be a crucial issue in small biopsies. Consequently, PD-L1 expression on CTCs may potentially shed light on the discrepancies derived from its use as a predictive biomarker using tumor tissue.
Recently, PD-L1 expression has been tested in CTCs of NSCLC. Almost all of the patients tested9 were PD-L1 positive, complicating the interpretation of the result and its clinical use. Overall, PD-L1-positive CTCs were detected in 69.4% of samples from an average of 4.5 cells/mL10. After initiation of radiation therapy, the proportion of PD-L1-positive CTCs increased significantly, indicating upregulation of PD-L1 expression in response to radiation11. Hence, PD-L1 CTCs analysis may be used to monitor dynamic changes of the tumor and immune response, which may reflect the response to chemotherapy, radiation, and likely immunotherapy (IT) treatments.
To date, CTCs isolation and PD-L1 characterization rely on various methods such as anti-EpCAM-coated magnetic bead-based CTC capturing, enrichment-free based assay, and size-based12,13 CTC capture assays. However, CTCs were only detected in 45%-65% of patients with metastatic NSCLC, thus limiting their ability to provide any information for more than half of metastatic NSCLC patients. In addition, CTC count was low in most of these studies using size-based approach10. Furthermore, this method has led to discrepancies such as the detection of CD45(-)/DAPI(+) cells with "cytomorphological patterns of malignancy" in the bloodstream of healthy donors. These concerns highlight the need for a highly sensitive method of CTC collection associated with immune-phenotyping of atypical CD45(-) cells from healthy whole blood using additional cancer biomarkers (i.e., TTF1, Vimentin, EpCAM, and CD44) in NSCLC.
Consequently, we evaluated a spiral microfluidic device that uses inertial and Dean drag forces to separate cells based on size and plasticity through a microfluidic chip. The formation of Dean vortex flows present in the microfluidic chip results in larger CTCs located along the inner wall and smaller immune cells along the outer wall of the chip. The enrichment process is completed by siphoning the larger cells into the collection outlet as the enriched CTC fraction. This method is particularly sensitive and specific (detection of around 1 CTC/mL of whole blood)14 and can be associated with customized immunofluorescence (IF) analyses. These tools will enable setting up of a positive threshold for clinical interpretation. A workflow is thus described that enables biologists to isolate and immunophenotype CTCs with a high rate of recovery and specificity. The protocol describes optimal use of the spiral microfluidic device to collect CTCs, the optimized IF assays that can be customized according to cancer type, and use of free open-source software for measuring and analyzing cell images to perform a semi-automatic numeration of the cells according to fluorescent staining. In addition, microscope multiplexing can be carried out depending on the number of fluorescent filters/markers available.
Samples were prospectively collected within the framework of the CIRCAN ("CIRculating CANcer") cohort based at the Lyon University Hospital following patient written consent. This study was integrated into the CIRCAN_ALL cohort. The study CIRCAN_ALL was recognized as non-interventional by the CPP South-East IV dated 04/11/2015 under the reference L15-188. An amended version was recognized as non-interventional on 20/09/2016 under reference L16-160. The CIRCAN_ALL study was declared to the IT and freedom correspondent of the Hospices Civils de Lyon on 01/12/2015, under the reference 15-131. Blood collection was performed when physicians observed the earliest indication of tumor progression.
NOTE: Use all the reagents and materials outlined in Table of Materials with the respective storage conditions for pre-analytical sample preparation and immunofluorescence assay. Substituting reagents and/or modifying storage conditions could result in suboptimal assay performance.
1. Decontamination of Spiral Microfluidic Device
NOTE: Decontamination of the spiral microfluidic device is a requirement to remove all immunofluorescence background generated from bacteria contamination, explore the cytomorphology of CTCs, and be able to differentiate them from normal immune cells. The protocol is optimized for blood samples collected in K2EDTA tubes within 6 h after blood sampling and enriched using the spiral microfluidic device in clean conditions. Using this assay for other types of samples (other biological fluids) may require additional optimization. This decontamination protocol should be done once per week.
2. Maintenance to Keep the Spiral Microfluidic Device Bacteria-free
NOTE: The routine maintenance should be done at the end of the day during the last cleaning step.
3. Pre-analytical Enrichment of CTC from Patient Blood Samples
4. Enrichment of CTCs from Patient Whole Blood with the Spiral Microfluidic Device
5. Immunofluorescence Staining
6. Acquisition of Immunofluorescent Images with Straight Fluorescent Microscope and Associated Software
7. Analysis of Immunofluorescent Images with Image Analysis Software
The first pre-requisite was to obtain uncontaminated (infectious agent-free) collections of CTCs for tissue culture and avoid IF background generated. The decontamination protocol enabled cleaning of all the pipes and pumps, and it resulted in the collection of CTCs with a good recovery rate without bacterial contamination. The enriched samples were compared without and with the decontamination protocol workflow of the spiral microfluidic device. To validate the decontamination protocol, the A549 cell line was used in absence of whole blood and enriched directly using the spiral microfluidic device. Without the optimized decontamination protocol, high bacterial contamination was observed in the tissue culture of enriched A549 cell line after only 24 h, which caused death and cytomorphological changes in eukaryotic cells (Figure 1B).
In contrast, after the cleaning protocol, living A549 cells were obtained by growing in 2D culture after 10 h of tissue culture and media removal and in 3D conditions (Figure 1B), as well as patient samples (Figure 1C). The potential CTC are identified with a red cross (Figure 1C).
Figure 2 recapitulates the complete workflow for immunofluorescence phenotyping of enriched CTCs from whole blood. It is composed of four major steps: whole blood sampling, CTC enrichment, immunofluorescence (IF) assay, and image analysis using software. Previously, the recovery rate of the spiral microfluidic device has been addressed14. Using fluorescent mimicking CTCs (mCTC), this recovery rate was established at 1.3 CTCs/mL whole blood14.
The present work focused on setting up the optimal conditions for IF analysis of enriched CTCs and downstream visualization (Figure 2). First, to test the specificity of the PD-L1 antibody, two cell lines were used: (1) PC3 high-positive-PD-L1 cell line and (2) SW620 low-positive-PD-L1 cell line. The cells were then enriched with the spiral microfluidic device and analyzed by IF. All cells were stained with the tumor anti-PanCK marker, white blood cell anti-CD45 marker, anti-PD-L1 (useful in lung cancer), and DAPI (nuclear dye). White blood cells were identified as positive for DAPI and CD45, while cancer cells were identified as positive for DAPI and PanCK and negative for CD45. The PC3 high-PD-L1-positive cell line was positively stained for PD-L1, while a lower PD-L1 expression was detected in the SW620 low-PD-L1-negative cell line.
Then, the following were compared: the (i) liquid IF staining assay, (ii) staining of CTCs directly deposited onto polylysine-coated slides, and (iii) IF staining of CTCs after cytospin on polylysine-coated slides. It was clearly observed that the recovery rate of CTCs depended on the type of protocol used (Figure 3A). In the liquid IF staining assay, the recovery rate was only 10% for the number of spiked mCTC was the lowest. This low recovery rate presents an issue for most patients with metastatic NSCLC, as it significantly limits the ability of these tests to isolate the few CTCs and provide phenotypic information. The second and third sections described (direct deposition of mCTC or CTC onto polylysine-coated slides without and with cytopsin) systematically had recovery rates exceeding 60% (Figure 3A).
Figure 3B shows representative images of these IF assays using whole blood samples from the same patient, either using the liquid IF staining assay or IF staining assay on polylysine-coated slides with cytospin. The enumeration of nuclei was clearly different between the two assays (Figure 3B). The nuclear DAPI staining provided the enumeration of total cells in the sample, and the biomarker staining enabled highlighting of the green the PanCK-positive cells, orange in the PD-L1-positive cells, and red in the CD45 residual white cells (Figure 3B).
Next, to cytologically differentiate white blood cells from tumoral cells, the shape of the nucleus has to be visualized, since it is characteristic of the cell type. Figure 3C demonstrates the outlines of the nuclei that are blurry and morphology that is unusual in the absence of the cytospin step. The optimized protocol thus included cytospinning of enriched CTCs on polylysine-coated slides followed by 4% paraformaldehyde (PFA) fixation, for preserving the slides before IF staining. This optimized protocol had similar recovery rates as the deposition of mCTC directly onto polylysine-coated slides (Figure 3A), even when very few cells were added. Since this additional step enable preservation of nuclear morphology (Figure 3C), granulocytes were identified with their multi-lobed nuclei, as well as tumoral cells (labelled with a red cross in the nucleus) with their nuclear abnormalities, malignancy patterns, and larger size compared to white blood cells.
After optimization of the IF protocol, a proof-of-concept was conducted using whole blood from metastatic patients. Samples were prospectively collected within the framework of the CIRCAN routine cohort based at the Lyon University Hospital. Blood collection was usually performed when physicians observed the earliest indication of tumor progression. All tumor cases were histologically or cytologically confirmed on FFPE biopsy specimens during the initial diagnosis. Here, CTCs analyses at progression were performed by investigators who did not have access to or prior knowledge of clinical data. Detailed pre-analytical considerations have been previously published15.
In Figure 4, Table 1, and Table 2, different findings are presented from patient samples. The CD45(+), PanCK(-), PD-L1(-) profile represents the immune cells. The residual count of white blood cells was shown to be strongly variable and dependent on the whole blood sample. The range in this small pilot cohort was 648-11,000 white CD45(+) cells (Figure 5A). Consequently, immediately after CTC enrichment, an enumeration of the collected cells was included to adjust the cellular density on the cytospin area at a density of 100,000 cells/cytospin (see section 6). This enabled performance of several cytospins per patient and optimization of the microscopic observation for manual enumeration and use of image analysis software pipeline.
In Figure 4A-C, Table 1, and Table 2, typical cases are reported in which residual white blood cell counts were highly different:
(i) The first profile is the CD45(-), PanCK(+), and PD-L1(+) presented in Figure 4A . Often the size of the cells is superior to 13 µm in diameter and the nucleus morphology is irregular, representing a cytomorphological pattern of malignancy. This population is likely composed of CTC.
(ii) The second profile is the CD45(-), PanCK(-), and PD-L1(+). As already reported, not all CTCs express the PanCK biomarker (Figure 4A).
(iii) The third profile is the CD45(-), PanCK(+), and PD-L1(-). As already reported, not all CTCs express the PD-L1 biomarker.
(iv) The fourth profile is the CD45(+), PanCK(+), and PD-L1(+) presented in Figure 4B. It represents the atypical activated immune cells in patient whole blood. This population has been described in several publications16,17,18 and represents approximately 5% of the total cells following enrichment. The presence of this population may increase the rate of false positive CTCs in a sample if the intensity of the CD45 signal is too low and morphology of the nucleus is not well-conserved. This strongly highlights the need for carrying out complementary tumoral biomarker staining, such as Vimentin and/or Epcam in this immunofluorescence assay.
(v) Finally, the last profile includes the unlabeled cells CD45(-), PanCK(-), and PD-L1(-), highlighted in Figure 4C. The nucleus in this population often shows cytomorphological patterns of malignancy, and the size is over 13 µm in diameter. The percentage of these cells in the samples is highly variable according to the patient whole blood. This highlights the need to use complementary tumoral biomarkers to confirm the tumoral pattern of this cell sub-population.
In Table 1 and Table 2, the cell count of 16 samples was reported from advanced metastatic NSCLC patients. The cells were classified according to the expression of the biomarkers. High variability was observed in the sub-populations obtained. As already reported in independent studies, CD45(-), PanCK(+), and PD-L1(+) profiles were found in most samples. Nevertheless, as the CTC population is highly heterogeneous, patient samples also contained CD45(-), PanCK(-), and PD-L1(+) sub-populations, the CD45(-), PanCK(+), and PD-L1(-) sub-populations and unlabeled cells CD45(-), PanCK(-), and PD-L1(-) sub-populations. The level of residual white blood cells was highly variable among analyzed samples.
To facilitate cell enumeration, a pilot pipeline was set up using the image analysis software for an automated analysis of the immunofluorescence images. The workflow is described in Figure 5. In this case, it is important to acquire high quality immunofluorescence images in terms of contrast and fluorescence intensity. Depending on the capacity of cluster calculation of the hardware, the image analysis pipeline can be applied to the complete merged image of the cytospin or on a representative area of the cytospin.
Here, based on the microscope, a semi-automated scan of the cytospin (X/Y; the Z focus is not included) area generated 150-200 merged images. These images can be merged together and directly analyzed using the image analysis pipeline. Nevertheless, this procedure is time- and calculation cluster resource-consuming, an important limitation for its routine use in laboratories. Therefore, based on prior experience in the field of cellular hematology, it was decided to analyze representative areas of each sample after verifying under a microscope that the distribution of cells was homogenous on the entire area of the cytospin. Then, 25% of the total area of the cytospin (around 40 tiles) was scanned with the florescence microscope to generate 40 x 4 independent images. The merged file was split by channels, and image files were automatically generated with the microscope software (see section 7; Figure 5A). These files were imported into an image analysis pipeline for analysis according to the parameters described (see section 8; Figure 5A).
In Figure 5B, we manually identified a representative image displaying four CTCs [CD45(-), PanCK(+), and PD-L1(+)] among 77 immune cells [CD45(+), PanCK(-), and PD-L1(-)]. Figure 5B illustrates how the image analysis software identified and enumerated the number of cells based on the DAPI staining. It also illustrates how the image analysis software counted the secondary objects. Finally, fluorescence intensities for each fluorescent channels were reported for all objects reported in the images.
The background was calculated and represented by the negative cells present in the sample. For example, the non-activated immune cells have low fluorescence intensity and enabled measuring of the background of the PanCK and PD-L1 staining. The fluorescent signal was deemed positive if the fluorescence intensity exceeded that of the background by two-fold (based on the analysis of four independent patient samples). Concerning CD45 staining, as the expression level of CD45 is highly variable in the white blood cells sub-populations, the threshold of positivity was set as low as possible. It was based on the analysis of images of 10 healthy whole blood stained with the CD45 antibody. The pilot analysis (n = 4) showed concordance between manual enumeration and image analysis software enumeration (Table 2). Each cell on the cytospin is identified by image analysis software and enables biologists to track the cell and manually confirm the results, if needed.
Figure 1: Overview of the workflow for decontamination of spiral microfluidic device instrument. (A) Three major steps included in the decontamination process (see protocol), illustrating the localization of the input and outputs of the instrument. (B) Representative images of A549 cell line enrichment before and after decontamination of the instrument. The impact of the presence of an infectious agent on the viability and morphology of collected cells is shown. In the presence of bacteria, cell morphology and viability was modified. Scale bar = 20 µm. (C) 3D cell culture of enriched patient samples of lung, prostate and breast cancer. The red cross corresponds to atypical cells. Scale bar = 20 µm. Please click here to view a larger version of this figure.
Figure 2: Overview of the workflow of immunofluorescence analysis from whole blood sampling to analysis of the fluorescence images. The major steps are shown as follows: blood collection for whole blood, CTC collection with spiral microfluidic device, immunofluorescence assay, and image analysis software. The choice of biomarker was driven by better identification of the various populations of cells observable on the cytospin slide (CD45 for immune cells, PanCK and PD-L1 for lung cancer cells). Please click here to view a larger version of this figure.
Figure 3: Recovery rate of three independent staining protocols. (A) Comparison of the recovery rate of mCTCs from liquid staining, direct staining of cell deposited onto polylysine-coated slides, and cell staining after cytospin on polylysine-coated slides. (B) Representative images of patient samples processed by liquid IF protocol and direct immunostaining protocol with the cytospinstep. Cells were stained with CD45 monoclonal antibody (clone HI30) Alexa Fluor 647; PanCK monoclonal antibody (clone AE1/AE3) Alexa Fluor 488; 4',6-diamidino-2-phénylindole (DAPI). Scale bar = 20 µm. (C) Representative images of DAPI staining of cell enrichment with and without the cytospin step. The morphology and size of the nuclei were shown using DAPI staining. Red cross highlights cells with abnormalities in the nucleus in the right-hand image. In the left-hand image, the image is fuzzy, since cells are not at the same level (x-, y-, and z-axes). Scale bar = 10 µm. Please click here to view a larger version of this figure.
Figure 4: Identification of cell profiles. (A) Representative images of patients with different CTC profiles. The fluorescence channels are presented separately. The merged images are shown on the left. They are stained with CD45 monoclonal antibody (clone HI30) Alexa Fluor 647; PanCK monoclonal antibody (clone AE1/AE3) Alexa Fluor 488; PDL-1 monoclonal antibody (clone 29E2A3) phycoerythrin; 4',6-diamidino-2-phénylindole (DAPI); arrows point to atypical cells. (B) Representative images of immunostaining of two patient samples with atypical white blood cells profiles. Cells were stained with CD45 monoclonal antibody (clone HI30) Alexa Fluor 647; PanCK monoclonal antibody (clone AE1/AE3) Alexa Fluor 488; PDL-1 monoclonal antibody (clone 29E2A3) phycoerythrin; 4',6-diamidino-2-phénylindole (DAPI). The image highlights the presence of immune cells stained with CD45(+), PanCK(+), and PD-L1(+). (C) The image highlights the presence of unlabeled cells (CD45(-), PanCK(-), and PD-L1(-). Scale bar = 10 µm. Please click here to view a larger version of this figure.
Figure 5: Overview of analysis of the fluorescent images. (A) The main steps are described: microscopy scanning, the channel split according to the fluorescence, and the import of files into image analysis software. (B) Description of the three different steps for the workflow of the image analysis software. Please click here to view a larger version of this figure.
Table 1: Manual enumeration of patient cell enrichment. Enumeration of cells based on DAPI staining. Enumeration of other objects based on FITC, PE and CY5 staining.
Table 2: Image analysis software enumeration of patient cell enrichment. Enumeration of cells based on DAPI staining. Enumeration of other objects based on FITC, PE and CY5 staining. Comparison of manual count and image analysis software enumeration.
Two major points were raised in the present study, the first with regards to performance of the workflow for its transfer to clinical applications, and the second concerning the decrease in subjectivity for the analysis of fluorescence images obtained.
A performant and optimized workflow for CTC enumeration was initially determined using customizable IF assay after cell enrichment via a CTC label-free microfluidic system (spiral microfluidic device). Using this workflow, a pilot study confirmed that all samples from metastatic NSCLC patients contained atypical cells, which were all CD45(-). They may alternatively be labeled with PanCK and/or PD-L1 biomarkers; however, they can also be completely negative for all tested biomarkers [CD45(-), PanCK(-), and PD-L1(-) as observed in the S19 sample (Table 2)]. This strongly highlights the need for additional biomarkers for phenotyping CTC sub-populations. Consequently, it has been proposed to add epithelial-mesenchymal biomarkers such as EpCAM, Vimentin, and N-Cadherin; markers of cancer stem cells including CD44 and CD133; and specific tumor markers including TTF1 for lung adenocarcinoma.
In the pilot study, the range of atypical cell was [40; >400] from 3.5 mL of whole blood. For 80% of the patient samples, the atypical cell count was over 50. Indeed, in cytological19,20,21 samples from Endo Bronchial Ultra Sonic Guide Trans Bronchial Needle Aspiration or CT-guided trans-thoracic punctures, PD-L1 analysis is suitable in most of samples, but a threshold of ≥100 tumor cells is commonly admitted to produce a statistical and clinical interpretation of the value. However, in the particular case of blood CTCs, it should be noted that the issue of spatial tumor heterogeneity is bypassed by contrast to small on-site tumor samples.
The second point was to avoid the impact of the handler on analysis of immunofluorescence images. Image analysis software of fluorescent images was thus set up to standardize cell enumeration and provide statistical data for these samples. This automated process highlighted the need for powerful calculation clusters for the analysis of all cells contained in the same sample. In addition, quality of the IF staining has to be in the same plane (to avoid use of confocal systems), and the density of cells on the cytospin must be calibrated to enable the image analysis software to recognize all cells separately on the slide. Finally, the results were not validated regarding clinical outcomes in a cohort of patients, but this point should be addressed in another dedicated study.
The authors have nothing to disclose.
This work was supported by research grants from AstraZeneca (London, United-Kingdom), Biolidics (Singapore) and the Ligue Contre le Cancer (Saone et Loire, France). The authors thank AstraZeneca and Biolidics companies for their financial support.
4',6-diamidino-2-phénylindole (DAPI) | Ozyme | BLE 422801 | Storage conditions: +4°C |
BD Facs Clean – 5L | BD Biosciences | 340345 | Bleach-based cleaning agent. Storage conditions: Room temperature |
Bleach 1% Cleaning Solution 100 mL | Biolidics | CBB-F016012 | Bleach. Storage conditions: Room temperature |
Bovine Serum Albumin (BSA) 7.5% | Sigma | A8412 | Storage conditions: +4°C |
CD45 monoclonal antibody (clone HI30) Alexa Fluor 647 | BioLegend | BLE304020 | Storage conditions: +4°C |
CellProfiler Software | Broad Institute | Image Analysis Software | |
Centrifuge device | Hettich | 4706 | Storage conditions: Room temperature |
Centrifuge tube 50 mL | Corning | 430-829 | Storage conditions: Room temperature |
Centrifuge Tube 15 mL | Biolidics | CBB-F001004-25 | Storage conditions: Room temperature |
ClearCell FX-1 System | Biolidics | CBB-F011002 | Spiral microfluidic device. Storage conditions: Room temperature |
Coulter Clenz Cleaning Agent – 5L | Beckman Coulter | 8448222 | All-purpose cleaning reagent. Storage conditions: Room temperature |
CTChip FR1S | Biolidics | CBB-FR001002 | Microfluidic chip. Storage conditions: Room temperature |
Cytospin 4 | ThermoFisher | A78300003 | Storage conditions: Room temperature |
Diluent Additive Reagent – 20 mL | Biolidics | CBB-F016009 | Storage conditions: +4°C |
EZ Cytofunnels | ThermoFisher | A78710003 | Sample chamber with cotton. Storage conditions: Room temperature |
FcR blocking Agent | Miltenyi Biotec | 130-059-901 | Storage conditions: +4°C |
Fetal Calf Serum (FCS) | Gibco | 10270-106 | Storage conditions: +4°C |
Fluoromount | Sigma | F4680 | Mounting solution. Storage conditions: Room temperature |
Fungizone – 50 mg | Bristol-Myers-Squibb | 90129TB29 | Anti-fungal reagent. Storage conditions: +4°C |
FX1 Input Straw with lock cap | Biolidics | CBB-F013005 | Straw. Storage conditions: Room temperature |
KovaSlide | Dutscher | 50126 | Chambered slide. Storage conditions: Room temperature |
PanCK monoclonal antibody (clone AE1/AE3) Alexa Fluor 488 | ThermoFisher | 53-9003-80 | Storage conditions: +4°C |
Paraformaldehyde 16% | ThermoFisher | 11490570 | Fixation solution. Storage conditions: +4°C |
PD-L1 monoclonal antibody (clone 29E2A3) – Phycoerythrin | BioLegend | BLE329706 | Storage conditions: +4°C |
Petri Dish | Dutscher | 632180 | Storage conditions: Room temperature |
Phosphate Buffered Saline (PBS) | Ozyme | BE17-512F | Storage conditions: +4°C |
Phosphate Buffered Saline Ultra Pure Grade 1X – 1L | 1st Base Laboratory | BUF-2040-1X1L | Storage conditions: Room temperature |
Pluronic F-68 10% | Gibco | 24040-032 | Anti-binding solution. Storage conditions: Room temperature |
Polylysine slides | ThermoFisher | J2800AMNZ | Storage conditions: Room temperature |
Polypropylene Conical Tube 50 mL | Falcon | 352098 | Storage conditions: Room temperature |
RBC Lysis Buffer – 100 mL | G Biosciences | 786-649 | Storage conditions: +4°C |
RBC Lysis Buffer – 250 mL | G Biosciences | 786-650 | Storage conditions: +4°C |
Resuspension Buffer (RSB) | Biolidics | CBB-F016003 | Storage conditions: +4°C |
Shandon Cytopsin4 centrifuge | ThermoFisher | A78300003 | Dedicated centrifuge. Storage conditions: Room temperature |
Silicon Isolator | Grace bio-Labs | 664270 | Storage conditions: Room temperature |
Sterile Deionized Water – 100 mL | 1st Base Laboratory | CUS-4100-100ml | Storage conditions: Room temperature |
Straight Fluorescent microscope Axio Imager D1 | Zeiss | Storage conditions: Room temperature | |
Surgical Sterile Bag | SPS Laboratoires | 98ULT01240 | Storage conditions: Room temperature |
Syringe BD Discardit II 20 mL sterile | BD Biosciences | 300296 | Storage conditions: Room temperature |
Syringe Filter 0.22 µm 33 mm sterile | ClearLine | 51732 | Storage conditions: Room temperature |
Zen lite 2.3 Lite Software | Zeiss | Microscope associated software |