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Supplementary Material
Method to investigate the distribution of water-soluble drug delivery systems in fresh frozen tissues using imaging mass cytometry
Nicole Strittmatter1,*, Richard M. England2, Alan M. Race3, Daniel Sutton1, Jennifer I. Moss4, Gareth
Maglennon5, Stephanie Ling1, Edmond Wong6, Jonathan Rose7, Ian Purvis7, Ruth Macdonald7, Simon
T. Barry4, Marianne B. Ashford2, Richard J. A. Goodwin1,8
1 Imaging and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge, CB2 0AA, U.K.
2 Advanced Drug Delivery, Pharmaceutical Sciences, R&D BioPharmaceuticals, AstraZeneca, Macclesfield SK10 2NA, U.K. 3 Institute of Medical Bioinformatics and Biostatistics, Philipps University of Marburg, 35037 Marburg, Germany 4 Bioscience, Discovery, Oncology R&D, AstraZeneca, Cambridge CB2 0AA, U.K.
5 Oncology Safety, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K.
6 Antibody Discovery and Protein Engineering, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K. 7 Animal Sciences and Technologies, Clinical Pharmacology and Safety Sciences, R&D BioPharmaceuticals, AstraZeneca, Cambridge CB2 0AA, U.K.
8 Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, U.K.
* Corresponding author, email: [email protected]
Contents:
Synthesis of metal-tagged S-Dend-DOTA drug delivery platforms and characterisation data, method description for custom antibody generation and stock concentrations, experimental scheme used for method development, IMC image of spleen used for antibody quantification, graph showing
antibody intensity after irradiation with different laser settings, composite images of kidney, lung and liver showing S-Dend distribution, optical images of ablation interface for different tissue types, further details for experiment on different section thicknesses (antibody panel, optical images, antibody abundance for CD206 and β-Catenin), calibration curves for S-Dend standard, additional information on image co-registration and analysis, graph showing % S-Dend+ cells.
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Synthesis of polysarcosine modified generation 5 L-lysine dendrimer has been described elsewhere.1
Synthesis of G5-PLL[P(Sar)40]32[DOTA-tris-butyl ester]32 SD40-DOTA(tBu3). G5-PLL[P(Sar)40]32[NH2.TFA]32 (200 mg, 0.0014 mmol) was dissolved in anhydrous
N,N’-Dimethylformamide (DMF) (2 mL) in a 10 mL reaction vessel purged with nitrogen and fitted with a stirrer bar. N,N’-Diisopropylethylamine (32 µL, 0.183 mmol) was added followed by a solution of 1,4,7,10-Tetraazacyclododecane-1,4,7-tris-tert-butyl acetate-10-acetic acid (56 mg, 0.0929 mmol) and 4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methyl-morpholin-4-ium (32 mg, 0.0927 mmol) dissolved in anhydrous DMF (1.5 mL). The reaction mixture was stirred overnight at room temperature. The reaction mixture was added to rapidly stirring tert-butyl methyl ether (TBME) (100 mL) to form a white precipitate. The solid was collected by vacuum filtration under nitrogen and then overnight at 40oC in a vacuum oven (50 mBar). The title product was obtained as a white solid. Yield = 166 mg, 79
%.
Synthesis of G5-PLL[P(Sar)40]32[DOTA]32 SD40-DOTA. SD40-DOTA(tBu3) (150 mg, 0.001 mmol) was
suspended in anhydrous dichloromethane (DCM) (2 mL) in a 10 mL round bottomed flask fitted with a nitrogen line and stirrer bar. Trifluoroacetic acid (1 mL) was added slowly resulting in the solid dissolving. The reaction was stirred for 5 h at room temperature. The solution was then added to rapidly stirring TBME (100 mL) to form a precipitate. The solid was collected by vacuum filtration and washed with additional TBME (2 x 10 mL) The solid was dried on the filter pad then dissolved in water (5 mL), frozen in dry ice and freeze-dried for 24 h. The title compound was obtained as a white solid. Yield = 146 mg, 100 %.
Synthesis of G5-PLL[P(Sar)40][DOTA-Tb]. A solution of Terbium (III) chloride (0.05 M) was prepared in HEPES buffer (0.1 M). SD40-DOTA (80 mg, 0.0006 mmol) was dissolved in the Terbium containing buffer (5 mL) and the solution stirred at room temperature overnight. The solution was filtered with a 0.45 µm PVDF syringe filter into a Vivaspin® 20 centrifugal concentrator (10 kDa) and washed with HEPES buffer (0.1 M, 3 x 10 mL), Na2EDTA (0.02 M, 1 x 10 mL) and deionised water (3 x 10 mL) all
centrifuged at 3000 ×g. The solution was finally frozen on dry ice and freeze-dried for 24 h. The title compound was obtained as an off-white solid. Yield = 69 mg, 83 %.
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Figure S1. A) 1H-NMR spectrum (500MHz, d
4-AcOH) and simplified structure for SD40-DOTA(tBu3)
precursor. The integration reveals 18 DOTA molecules/star polymer. B) Mass spectrometry analysis for SD40-DOTA-Tb by MALDI-TOF-MS, C) Number average hydrodynamic size of SD40-DOTA-Tb from Dynamic Light Scattering (10 mg/mL in PBS).
Table S1. Stock concentrations of custom antibodies.
Target Clone Metal tag Stock conc. [mg/mL]
CD68 FA-11 145Nd 0.66 β-Catenin D13A1 146Nd 0.42 NKp46 Polyclonal 147Sm 0.43 Collagen I Polyclonal 150Nd 0.53 Desmin Poly 152Sm 0.50 CD11c D1V9Y 153Eu 0.31 F4/80 CI:A3-1 155Gd 0.47 CD163 TNKUPJ 156Gd 1.50
αSMA Polyclonal 163Dy 0.46
CD31 390 164Dy 0.50
Tenascin C Polyclonal 167Er 0.18
CD3 CD3-12 171Yb 0.63
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Custom antibody labelling
Custom antibodies were labelled according the manufacturer's instruction. To summarise, antibodies were reduced and buffer exchanged into the recommended buffers in 0.5 mL spin concentrators. Where applicable, lyophilised antibodies were rehydrated with PBS, washed 3 times in 0.5 mL spin concentrators with PBS before reduction and washing according to the manufacturer's instruction. Separately, MAXPAR polymer was rehydrated then incubated with lanthanide metal before removing excess metals and buffer change. Reduced antibody and lanthanide metal bound polymer were mixed and incubated for 1.5 hrs at 37 °C before washing and storage as
recommended. All custom and commercial antibodies were validated in house using conventional IHC as well as corresponding IMC staining and assessed by a veterinary pathologist.
Figure S2. Experimental setup. Imaging areas in each imaging run are shown in half-transparent green.
Figure S3. Composite image of CD3 (cyan), B220 (blue), CD206 (green) with tissue classifier into red pulp (green) and white pulp (blue) overlaid half-transparently. Training regions are indicated by
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Figure S4. Optical scanned images (20x magnification) recorded at the ablation interface after the first ablation step for A) duodenum, B) kidney, C) liver and D) spleen tissues. A zoom was added to allow visualisation of the laser spot craters.
Figure S5. Graph showing percentage of signal observed in irradiated vs non irradiated tissue in spleen over entire area - signal from red or white pulp only chosen based on where the antibody was
more abundant.
Figure S6. A) 10-point calibration line of 1uL spots of 159Tb-DOTA S-Dend solutions at
concentrations given in the x-axis spotted onto liver tissue over an area of 1x2cm and analysed without further processing (3 more dilutions were performed but not detected and thus excluded). For each spot, a comparable area spanning from the centre to the outer rim of each spot was analysed and averaged to obtain mean intensities. B) zoom into lowest 5 calibration points in A.
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Table S2. Antibody panel used for data on different section thicknesses shown in Figure 2B+C.
Target Clone Metal tag Host Species Dilution
Dendrimer 250kDa 141Pr
Vimentin D21H3 143Nd Rabbit 100
B220 RA3-6B2 144Nd Rat 75
CD68* FA-11 145Nd Rat 100 (0.61 mg/mL)
β-Catenin* D13A1 146Nd Rabbit 100 (0.61 mg/mL)
NKp46* Polyclonal 147Sm Goat 50 (0.62 mg/mL)
Pan-CK C11 148Nd Mouse 100
Collagen 1* Polyclonal 150Nd Goat 200 (0.48 mg/mL)
Ly6G 1A8 151Eu Rat 50
Desmin* Poly 152Sm Goat 100 (0.5 mg/mL)
CD11b M1/70 154Sm Rat 50
F4/80* CI:A3-1 155Gd Rat 25 (0.12 mg/mL)
CD163* TNKUPJ 156Gd Rat 25 (0.68 mg/mL)
E-Cadherin 24E10 158Gd Rabbit 75
Dendrimer 150kDa 159Tb
αSMA* Polyclonal 163Dy Rabbit 400 (0.36 mg/mL)
CD31* 390 164Dy Rat 75 (0.62 mg/mL)
Dendrimer 75kDa 165Ho
EpCam (CD326) G8.8 166Er Rat 50
Tenascin C* Polyclonal 167Er Rabbit 100 (0.21 mg/mL)
CD206 C068C2 169Tm Rat 50
MHCII (I-A/I-E) M5/114.15.2 174Yb Rat 50
CD45 30-F11 175Lu Rat 100
ATPase* EP1845Y 89Y Rabbit 75 (0.9 mg/mL)
DNA 191Ir/193Ir 400
* custom antibodies, stock concentration in brackets
Figure S7. Dependence of mean intensity of A) CD206 and B) β-Catenin over the tissue section on laser power and tissue thickness in the second ablation step (IMC run2).
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Figure S8. Optical scans of liver tissue sections at different thicknesses post initial imaging step (IMC run1), each section was ablated at a 2-2 (x-y) step height at laser settings of 0, 2 and 4db.
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Figure S9. IMC images of additional tissues A) lung, B) kidney and C) liver. Initial panel shows image of second imaging step only with area of initial imaging step highlighted by a box. Second panel shows manual composite images of results of the second and first imaging steps. Third panel shows an image of the ablation interface of each tissue, location indicated in second image by yellow dashed box.
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Image registration
There are two options for aligning the two IMC experiments, depending on whether a common marker is present between the two experiments. If a common marker is present, such as 159Tb in this example, and the imaged areas overlap, it is possible to use the common ion/channel images (159Tb) to align the datasets.
If such a common marker is not present, then additional information from the instrument can be used to aid the registration process. The instrument uses an optical image (either acquired by the system, or input by the user) to define the location of the acquisition in 'slide' coordinates (in microns, with (0,0) being one corner of the slide). The spatial (slide) coordinates of both the optical image(s) and the IMC acquisition are recorded alongside the data. This provides a means to convert the IMC acquisition coordinates to the optical image space (via the slide coordinates). To align two IMC datasets, the two optical images from each experiment are then registered (as they are from the same slide, and same modality, this is a comparatively simple task) and then the IMC acquisition from one acquisition can be transformed via the registered optical images to the space of the second IMC acquisition.
We evaluated both methods with the use of a common marker (results shown in Table S3), which is producing slightly more accurate results. Evaluation was performed by exporting 159Tb channel images from each dataset (using a modified version of SpectralAnalysis2, see Figure S10A for
respective image overlay), annotating the same feature in each image (performed using QuPath v0.2.33, see test regions in Figure S10B), exporting the annotations to GeoJSON4, transforming the
annotations from one image space to the other using the transform determined from the registration process and comparing with two different metrics: i) Hausdorff distance5, which
measures the largest distance between two points for two regions of interest, giving an estimate of the worst case feature mis-alignment, and ii) Euclidean distance between two corresponding control points.
Table S3 shows single pixel accuracies are within 1-2 pixel (assuming 2 µm for the largest pixel size dataset) for both alignment options, indicating that even if in future studies there is no common ion image between the first and second dataset, use of the optical image as an intermediary in the registration process can enable accurate (within the size of the largest pixel in the acquired data) alignment.
We used the same method to evaluate the registration accuracy when a serial section is used (rather than the proposed method using the same section, see Figure S11A for resulting image overlay). In this case, the accuracy is considerably poorer, shown in Table S4 (4x worse when evaluating regions with the Hausdorff distance and approximately 8 pixels error for selected points, when considering largest pixel sizes of 2 µm, test regions shown in Figure S11B). This can largely be attributed to the fact that, due to being a serial section, some features are no longer in the same position relative to others (3D heterogeneous tissue) and we are employing rigid registration here. Non-rigid
registration methods exist to deform images non-linearly (compressing and stretching one region more than another) to enable a better fit of the data, but these methods have their own problems when it comes to subsequent data analysis. As different regions of the image are modified in different ways, each pixel can cover a vastly different size area in the transformed image, making direct comparison between datasets challenging. As such, the ability to use the same section, and therefore rigid registration techniques, as presented in this manuscript guarantees that all pixels in the transformed image cover equal sized areas.
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Figure S10: A) Registration result when using the same tissue section, using an optical image (so called 'panorama' scans) from each acquisition as an intermediary. The 159Tb ion is shown for both datasets. B) Regions used to evaluate the registration workflow for datasets acquired from the same section.
Table S3. Evaluation of the registration accuracy using optical images (so called 'panorama' scans) from each acquisition as an intermediary ('Optical') or using a common ion image (159Tb) between acquisitions ('Direct'). Evaluation performed using both the Hausdorff distance (which measures the largest distance between two points for two regions of interest, giving an estimate of the worst case feature mis-alignment) or Euclidean distance between pairs of manually selected control points (representing the same feature, to give an average pixel error)
Method
Hausdorff distance (µm) Euclidean distance (µm)
Region 1 Region 2 Region 3 Mean Point 1 Point 2 Point 3 Point 4 Mean Optical 5.39 5.10 5.39 5.29 1.30 1.89 1.47 2.52 1.80
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Figure S11: A) Registration result when aligning serial tissue sections, using the "best case" registration workflow, where the same ion is detected in both datasets. 159Tb is used (shown) for the alignment. B) Regions used to evaluate the registration workflow for datasets acquired from serial sections.
Table S4: Evaluation of the registration accuracy using a common ion image (159Tb) between acquisitions ('Direct'). Evaluation performed using both the Hausdorff distance or Euclidean distance between pairs of manually selected control points.
Method
Hausdorff distance (µm) Euclidean distance (µm)
Region 1 Region 2 Region 3 Mean Point 1 Point 2 Point 3 Mean Direct 25.63 16.16 25.50 22.43 17.72 13.45 16.12 15.76
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Figure S12. Positive markers for all cells that were found to be 159Tb positive. A single cell can be positive of several markers.
Group CD11b+ F4/80+ or CD68+ includes eosinophils, monocytes, macrophages as well as dendritic cells. Inclusion/Exclusion of further markers is necessary to further identify immune cell subtypes. Identification criteria for macrophages were CD45+ CD11b+ CD68+ or F4/80+ CD11c- while dendritic cells were CD45+ CD11b+ CD68+ or F4/80+ CD11c+. Phagocytotic macrophages were CD68+ or F4/80+ CD206+ CD163+.
Page | S13 References
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