Deep learning, 3D ultrastructural analysis reveals quantitative differences in platelet and organelle packing in COVID-19/SARSCoV2 patient-derived platelets

Figures & data

Table I. Demographics and relevant clinical parameters for COVID-19 patients and Control blood donors.

Control Donors	Control Donor 1	Control Donor 2	Control Donor 3
Age [Years]	28	27	40
Ethnicity	White	White	Southwestern Asian
Sex	Male	Male	Female
BMI [kg/m^2]	25	35	25
COVID Patients	COVID-19 Patient 1	COVID-19 Patient 2	COVID-19 Patient 3
Age [Years]	54	44	49
Ethnicity	White	Black	White
Sex	Female	Male	Male
BMI [kg/m^2]	59.1	39.6	32
Blood Draw Post-Diagnosis [Days]	22	33	12
Discharge after admission to hospital [Days]	40	36	N/A
White Blood Cell Count [cells x 10^9/L]	19.5	23.9	9.8
Hemoglobin [g/dL]	9	11.4	9.1
Platelet Count [cells x 10^9/L]	189	345	218
Deceased [Days after diagnosis]	No	No	Yes (46)
Previous Medical History	Morbid obesity and traumatic glaucoma secondary to a gunshot wound	Gout and obesity	Type 1 diabetes, hypertension, and obesity
Complications	Hospital-acquired pneumonia with Haemophilus influenzae, bacteremia with Streptococcus constellatus and Fusobacterium, acute kidney injury requiring continuous renal replacement therapy, diabetic ketoacidosis due to newly diagnosed type 2 diabetes, an outbreak of herpes simplex virus, and a deep venous thrombosis	Acute kidney injury requiring hemodialysis, necrotizing pancreatitis, a superinfected sacral ulcer, cryptogenic organizing pneumonia, oral thrush, and newly diagnosed hypertension and type 2 diabetes	Acute kidney injury, a subarachnoid hemorrhage, hospital-acquired pneumonia with Pseudomonas, and an MRSA infection

Figure 1. FIB-SEM raw images and 3D surface rendered models of platelets from Control and COVID-19 patients. x-y orthoslices and 3D surface renderings of 17 platelets from each of 3 Control and 3 COVID-19 patients are shown. Scale bars (2 µm) as shown.

Figure 2. Organelle morphology in platelets from Control and COVID-19 patients. Images with two different magnifications are shown of platelets from a Control (Control donor 3, left) and a COVID-19 patient (COVID 2, right). Identified features are indicated: mitochondria (MIT), α-granules (AG), open canalicular system (OCS), dense tubular system (DTS), possible neutrophil extracellular traps (NETS?), and apoptotic bodies (apoptosis).

Figure 3. Platelet volume measurements from 3D deep learning segmented FIB-SEM images of platelets from Control and COVID-19 patients. (A) depicted is an example of how the cell membrane segmentation was performed. The left image is a representative x-y orthoslice, and the right shows the segmentation of the platelet plasma membrane with the measured interior, highlighted in green. The long and short axis are indicated. Long (B) and short (C) axis lengths for each platelet were plotted (Control 1, n = 101; Control 2, n = 103; Control 3, n = 102; COVID-19 patient 1, n = 107; patient 2, n = 101; patient 3, n = 100). (D) the long-to-short axis ratios were calculated from the values in (B) and (C). For comparison, the ratios were calculated and plotted using published data from platelets that had been immediately fixed.²⁰ in aggregate, the COVID-19 axis ratios were significantly higher compared to controls (p = .001), but intra-patient comparisons showed no differences (p > .05) except when comparing COVID-19 patient 1 to patients 2 and 3 (p < .05). (E) platelet volumes (µm³) were also calculated and plotted for each platelet analyzed. Volumes for immediate fixed platelets were also calculated and plotted. In aggregate, the volumes of platelets from COVID-19 platelets were significantly lower than Control (p < .001). Intra-sample comparisons were significant when comparing the platelets from COVID-19 patient 3 with patient 1 and 2 (p < .05) and Control platelets from donor 2 with those from donor 1 and 3 (p < .05). Other comparisons were not significant. For (B-E) solid lines indicate averages, while dashed lines indicate average ± one standard deviation. (F) summary statistics for the measurements in (B-E). The average, standard deviation, and n for each subject were listed for long axis, short axis lengths, axis ratios and platelet volumes. For multiple group comparison Welch’s ANOVA was used and for the one-to-one comparisons Welch’s t-test was used. Note: * is for p < .05, ** is for p < .005, and *** is for p < .0005.

Figure 4. Deep learning and manual analysis of α-granules (AGs) in platelets from Control and COVID-19 patients. Analysis of platelets from all three Control samples and COVID-19 samples from patients 2 and 3 were done by deep learning segmentation. COVID-19 patient 1 platelets were analyzed by manual segmentation. (A) depicted is an example of how the platelet AG were analyzed. The left image is a representative x-y orthoslice, and the right shows the segmentation of the platelet AG, highlighted in blue. The number of AGs per platelet (B), AG volumes (C), and the ratios of total AG volume-to-platelet volume were calculated (D). The data for each platelet analyzed was plotted. In aggregate, platelets from COVID-19 patients had fewer AGs compared to controls (p = .022). Intra-sample comparisons showed no significant differences (p > .05) except when comparing COVID-19 patient 2’s platelets with those from COVID-19 patient 1 and 3 (p < .05). In aggregate, AG volumes in platelets from COVID-19 patients were significantly lower compared to Control (p = .001). Intra-sample comparisons showed no significant differences (p > .05) except when comparing Control 1 against Control 2 and 3 (p < .05). In aggregate, the total AG-to-platelet volume ratios were significantly higher in platelets from COVID-19 patients (p = .016). Intra-sample comparisons showed no significant differences (p > .05) except when comparing Control 1 against Control 2 and 3 (p < .05). In (B-D) solid lines indicate averages, while dashed lines indicate one standard deviation from average. (E) AG count, AG volume and total AG-to-platelet volume ratio with standard deviations, for each patient and donor are summarized. n is the number of platelets analyzed per sample. For multiple group comparison Welch’s ANOVA was used and for the one-to-one comparisons Welch’s t-test was used.

Table II. Comparative quantitative outcomes of COVID-19 patient platelet 3D ultrastructural analysis.

COVID vs CONTROL (Respective Samples Pooled Together)
Metric	Sample Size Control	Mean Control ± SD	Sample Size COVID	Mean COVID ± SD	Percentage Difference [(COVID-Control)/ Control] X 100%	P-value Welch’s T-test
Axis Ratio (Long/Short) per Platelet	N = 306	1.6 ± 0.3	N = 241	1.7 ± 0.3	COVID is greater by 6.3%	P = .001
Long Axis per Platelet (µm)	N = 306	4.1 ± 0.6	N = 308	3.7 ± 0.7	COVID is less by 9.8%	P < .001
Short Axis per Platelet (µm)	N = 306	2.5 ± 0.5	N = 308	2.2 ± 0.5	COVID is less by 12.0%	P < .001
Mean Platelet Volume (µm^3)	N = 306	9.5 ± 4.3	N = 308	6.2 ± 3.3	COVID is less by 34.7%	P < .001
α-Granule (AG) per Platelet	N = 306	53 ± 31	N = 231	47 ± 29	COVID is less by 11.3%	P = .022
α-Granule Volume per Platelet (µm^3)	N = 306	0.63 ± 0.45	N = 231	0.52 ± 0.38	COVID is less by 17.5%	P = .001
Individual α-Granule Volume (Mean, µm^3)	N = 17392	0.0121 ± 0.0115	N = 10799	0.0122 ± 0.0158	COVID is greater by 0.8%	P = .075 (NS)
Volume Ratio per Platelet (α-Granule/Platelet)	N = 306	0.066 ± 0.040	N = 231	0.084 ± 0.050	COVID is greater by 27.3%	P = .016
Mitochondria per Platelet	N = 33	10.6 ± 4.5	N = 30	10.7 ± 4.9	COVID is greater by 0.9%	P = .833 (NS)
Mitochondria Volume per Platelet, (Mean, µm^3)	N = 33	0.22 ± 0.11	N = 30	0.16 ± 0.04	COVID is less by 27.5%	P = .038
Individual Mitochondria Volume (Mean, µm^3)	N = 366	0.022 ± 0.005	N = 382	0.017 ± 0.003	COVID is less by 22.6%	P < .001
Volume Ratio per Platelet (Mito/Platelet)*	N = 33	0.021 ± 0.003*	N = 30	0.026 ± 0.004*	COVID is greater by 21.0%	P = .024

P values were calculated for the stated N values. * Mitochondrial parameters were determined by manual segmentation for a subset of cells. Volume ratios are calculated for that subset (see Figure 5).

Figure 5. Manual segmentation analysis of mitochondria in platelets from Control and COVID-19 patients. (A) depicted is an example of how the platelet mitochondria were analyzed. The left image is a representative x-y orthoslice, and the right shows the segmentation of the platelet mitochondria, highlighted in magenta. The number of mitochondria per platelet (B), mitochondrial volumes (C), and the ratios of total mitochondrial volume to platelet volume were calculated (D). In aggregate, platelets from COVID-19 patients had similar numbers of mitochondria (p = .833). Intra-sample comparisons showed no significant differences (p > .05) except when comparing mitochondrial numbers from Control 1’s platelets with that from Control 2 and 3 (p < .05) and COVID-19 patient 1’s platelets with those from COVID-19 patient 2 and 3 (p < .05). In aggregate, platelets from COVID-19 patients had significantly lower total mitochondria volumes per platelet compared to Control (p = .038). Intra-sample comparisons showed no differences across all comparisons (p > .05). In aggregate, platelets from COVID-19 patients had significantly higher total mitochondria-to-platelet volume values compared to Control (p = .024). Intra-sample comparisons showed no differences across all comparisons (p > .05). In (B-D) solid black lines indicate sample averages, and the dashed lines indicate one standard deviation from average. (E) mitochondrial count, mitochondria volume and total mitochondria-to-platelet volume ratio with standard deviations, for each patient and donor are summarized. n is the number of platelets analyzed per sample. For multiple group comparison Welch’s ANOVA was used and for the one-to-one comparisons Welch’s t-test was used.

Figure 6. Comparative histogram analysis of platelet heterogeneity in COVID patient, Control and immediate-fixed samples (A-E). Histograms of platelet volumes, platelet axis ratios, AG counts, AG volumes and total AG-to-platelet volume ratios for platelets from COVID-19 patients (top row, red), from Control donor platelets (middle row, blue) and from immediate-fixed platelets (plotted from raw data in Pokrovskaya et al.²⁰; bottom row, green). (F) graphical depiction of the structural differences between platelets from control donors and COVID-19 patients. Statistical analysis reveals that the α-granule/mitochondrial-to-platelet volume ratio is significantly greater in COVID-19 patient platelets indicating a more densely packed and compact platelet. The COVID-19 patient platelets were 35% smaller, and most of the differences in organelle packing density were due to a decreased platelet size, and not due to organelle count or volume. The cartoon image is illustrative and not strictly to scale.

Pokrovskaya ID, Yadav S, Rao A, McBride E, Kamykowski JA, Zhang G, Aronova MA, Leapman RD, Storrie B. 3D ultrastructural analysis of α-granule, dense granule, mitochondria, and canalicular system arrangement in resting human platelets. Res Pract Thromb Haemost. 2020;4(1):72–85. doi:10.1002/rth2.12260.

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