Supplementary Materials for

Imaging mitochondrial dynamics in human skin reveals

depth-dependent hypoxia and malignant potential for diagnosis

Dimitra Pouli, Mihaela Balu, Carlo A. Alonzo, Zhiyi Liu, Kyle P. Quinn,

Francisca Rius-Diaz, Ronald M. Harris, Kristen M. Kelly,

Bruce J. Tromberg, Irene Georgakoudi*

*Corresponding author. Email: irene.georgakoudi@tufts.edu

Published 30 November 2016, Sci. Transl. Med. 8, 367ra169 (2016)

DOI: 10.1126/scitranslmed.aag2202

This PDF file includes:

Materials and Methods

Fig. S1. Automated image analysis steps before quantitative extraction of cellular-

related biomarkers.

Fig. S2. Setup for dynamic media oxygen saturation and live oxygen saturation

measurements.

Fig. S3. Automated digital filter–based identification of endogenous TPEF

contributions from non-NADH chromophores.

Fig. S4. Detailed mitochondrial clustering profiles.

Fig. S5. The depth-dependent mean PSD variance is correlated with the

corresponding N/C ratio (nuclear-to-cytoplasmic ratio) variation.

Fig. S6. Organization patterns in stained mitochondria images of 2D cultures of

undifferentiated, differentiated, and apoptotic cells.

Fig. S7. Mitochondrial organization distinguishes benign from malignant

macroscopically suspicious sites.

www.sciencetranslationalmedicine.org/cgi/content/full/8/367/367ra169/DC1

Supplementary Materials

Materials and Methods

In vivo arterial occlusion – reperfusion measurements

TPEF images (512 × 512 pixels; 200 × 200 μm) of a human volar forearm under blood-

supplied oxygen deprivation conditions were acquired according to an approved UC Irvine

IRB protocol as described previously (30). Since the primary goal of these studies was to

evaluate the depth-dependent cellular response due to hypoxia, we avoided introducing inter-

subject variability by acquiring all in vivo occlusion-reperfusion measurements from a single

volunteer. Measurements from a single optical layer were recorded during each experiment

and different locations of the volar arm were imaged each time. A total of three upper and

three deeper cell layers were imaged during six different repeats of the occlusion-reperfusion

in vivo studies. Briefly, the participant’s volar forearm was imaged continuously for 9 min

using a laser-scanning based clinical multiphoton tomograph, MPTflex (JenLab GmbH).

Images were recorded before, during, and after arterial occlusion. An image was captured

every 10 seconds for a total of 180 sec for each phase. For the occlusion phase, arterial-

venous occlusion was induced by inflating an arm cuff placed on the participant’s bicep to

220-mm Hg pressure. For each individual experiment a fixed layer of epidermal cells was

recorded, within either 20–25 μm (upper layer) or 35–40 μm (lower layer) from the skin

surface. For this work, the excitation wavelength was tuned to 765 nm and NADH

fluorescence was detected over a broad spectral range (410–650nm). Laser power was 5 mW

at the surface of the skin.

In vitro hypoxia-reperfusion measurements

Primary neonatal human epidermal keratinocytes (NHEKs) were purchased from Lonza and

cultured on 12well collagen coated glass bottom plates (Mattek) until reaching confluence.

Cell cultures were sustained and imaged in commercially available keratinocyte growth

medium (KGM Gold BulletKit, Lonza). For the control (normoxic) group, imaging samples

were kept in a humid microincubator chamber maintained at 37 °C and 5% CO2 (Okolabs).

For the hypoxic group, a custom setup (fig. S2a) was built in-house to permit direct perfusion

of the well’s media, while allowing simultaneous temperature and oxygen saturation

measurements with a temperature probe and a needle-type oxygen microsensor, respectively

(PreSens). The construct isolates the well of interest and delivers gas within the culture media

through multiple immersed small gauge needles that face the perimetric edge of the well. The

treated group was perfused with nitrogen to induce hypoxia. After O2 saturation had reached

and stabilized below 1%, nitrogen perfusion was decreased to minimal levels, enough though

to sustain the media O2 saturation during the hypoxic period below 1% at all times (fig. S2B).

At the end of the hypoxic period, nitrogen flow was ceased and swapped with air and 5% CO2

to allow for normoxia recovery. After the latter was accomplished, gas flow was discontinued

for the remaining imaging period. Each experiment lasted 30 min in total and comprised four

phases, namely the baseline or Before phase lasting from 1 to 5 min, the hypoxic or During

phase lasting from 5 to 16 min, the early reperfusion or After Early phase lasting from 16 to

23 min, and the late reperfusion of After Late phase lasting from 23 to 30 min. TPEF images

(512 × 512 pixels; 238 × 238 μm) were captured every 2 minutes under stable media oxygen

saturation conditions (fig. S2B) on a Leica TCS SP2 confocal microscope equipped with a

Ti:sapphire laser (Spectra Physics). Samples were excited with 755 nm and imaged using a

63x/1.2 NA water immersion objective (Leica Microsystems, Germany). TPEF images were

acquired at 460 ± 20 nm. One field over time was followed during the entire experiment and

at least 3 images were captured for each experimental phase. The experiment was

independently replicated at least 4 times for both the hypoxic and normoxic group. All

acquired images were normalized for power and detector gain prior to processing as described

previously (19) and incident power at the focal plane during the acquisition was less than

23mW.

Image analysis of in vivo and in vitro time-dependent hypoxia – reperfusion

measurements

Image processing was performed in ImJ and Matlab. A series of analytical steps were

implemented to quantitatively extract the cellular related biomarkers. For nuclear and

interstitial space removal (fig. S1a) a custom bandpass filter was created by combining 3

separate bandpass filters. The first bandpass filter was formed by multiplying a Gaussian

highpass ( = 0.01 μm–1) and a Gaussian lowpass ( = 0.1 μm–1) filter. The second filter was

also a combination of two gaussians with the highpass = 0.021 μm–1 and the lowpass =

0.143 μm–1, respectively. The final bandpass filter was created by combining 3rd order

Butteworth lowpass and highpass filters. The highpass frequency cutoff was set to 0.021

μm–1, whereas the lowpass frequency cutoff was set to 0.2 μm–1. After each individual

bandpass filter was computed, they were multiplied to create the final custom bandpass filter.

The Butterworth filter was chosen as a complementary finer filtering approach, since it

displays a sharper apodization around the cutoff frequency range when compared with a

Gaussian one. The combination of Gaussian and Butterworth filters in the selected frequency

ranges, resulted in the minimization of ringing artifacts in the image space, while providing

enough selectivity to isolate the size range of cytoplasmic image features observed within the

healthy epithelium. Otsu’s auto-thresholding function was utilized to reduce each final

bandpass filtered image into a binary mask, consisting of cytoplasmic associated space (image

area kept) and background (image area discarded). A circular binary mask (fig. S1A) was also

applied universally prior to intensity and mitochondrial clustering extraction, to eliminate dim

image corner artifacts observed in a large number of in vivo acquired images. The mask was

applied also to the in vitro data, to avoid discrepancies, not supported by biological relevance,

in the collective analytical process.

After the isolation of image features attributed primarily to mitochondrial features

(fig. S1A), the mean NADH intensity was calculated by summing the pixel fluorescence

intensity values for each image and then normalizing by the relevant isolated image area. For

plotting purposes, the mean NADH intensity of the cellular data (Fig. 2B) was also

normalized for each treatment phase by the corresponding group’s baseline intensity.

After the isolation of mitochondrial features within the images, we proceeded with an

automated digital object cloning (DOC) technique, as described in detail previously (28, 29).

The cloning algorithm was utilized to only fill the intensity gaps produced by the nuclear and

interstitial feature removal, without overwriting any foreground pixels. This was done to

eliminate large-scale feature artifacts that can affect the evaluation of the mitochondrial

clustering (fig. S1C). The DOC process was repeated in this study 5 times for each image and

then the average PSD from the resulting images was calculated. An equation of the form:

R(k) = Ak–β was then fit to the region of the PSD where spatial frequency, k, was lower than

0.1 μm−1 for the cultured keratinocytes (corresponding to features smaller than 10 µm) and

0.118 μm−1 for the in vivo data (corresponding to features smaller than 8.5 µm). The absolute

slope value of the fit represents the degree of the mitochondrial clustering metric (β).

Increased β in our study represents more clustered (fragmented) mitochondrial formations

(28, 29). The slight difference of the fitting range between the in–vitro and in–vivo data was

based in each data set on the average observed distance from the nuclear edge to the

corresponding cytoplasmic membrane edge. As the human epithelial cells vary in size as a

function of depth, the mean of those distances from all different strata (fig. S4B) was used.

For plotting purposes, the mean mitochondrial clustering of the cellular data (Fig. 3B) was

normalized for each treatment phase by the corresponding group’s baseline mean

mitochondrial clustering.

Two additional processing steps (fig. S1B) were performed only for the in vivo

occlusion-reperfusion data, prior to the DOC and mitochondrial organization calculation

described above. Because of a gradient of higher intensity values observed towards the center

of all images, especially during the occlusion phase, an intensity normalization approach was

applied in order to minimize large scale intensity artifacts during the DOC and PSD analysis.

The method was based on object connectivity. Briefly, for each identified connected

component within each image (fig. S1B), which comprised usually of one or a few cells, the

sum of its pixel intensities was calculated. Then, each participating pixel within the object

was normalized by the intensity sum (fig. S1B), yielding uniform average intensities (equal to

1) for all objects within the image. For all other mitochondrial clustering evaluations, the

object connectivity and normalization steps were omitted, since the intensity gradients across

the image fields used for analysis appeared less significant and didn’t confound the analysis

results. However, it is possible that incorporation of further pre-processing algorithms that

account for intensity variations introduced in the images as a result of optical aberrations may

further enhance the diagnostic performance of the mitochondrial clustering analysis.

In vitro hypoxia FLIM studies

NHEKs, purchased from Lonza, were cultured on 12-well collagen coated glass bottom plates

(Mattek) until reaching confluence. Cell cultures were sustained and imaged in commercially

available Keratinocyte Growth Medium (KGM Gold BulletKit, Lonza). The FLIM dynamic

experiments had two phases, namely the baseline normoxic phase and the hypoxic phase.

After a baseline measurement was captured from each well, nitrogen was perfused to induce

hypoxia using the cell culture hypoxia custom built set up described previously. After O2

saturation had reached < 1%, nitrogen perfusion was decreased to minimal levels, enough

though to sustain the media O2 saturation during the hypoxic period below 1% at all times.

Then the hypoxic FLIM measurement was acquired from the same well. Three independent

wells were imaged and one random field per well was captured for each experimental phase

respectively. FLIM imaging was performed on a custom-built microscope with a 40× (NA

1.1) water-immersion objective (Leica Microsystems). Two-photon excitation was provided

by Ti: sapphire laser (Mai Tai; Spectra Physics) tuned to 755 nm. Emission events were

registered by a photomultiplier tube (H7422P-40; Hamamatsu Photonics) coupled to a time-

correlated single photon counting system (SPC-150; Becker and Hickl). To isolate NADH

fluorescence, a 460 ± 20 nm emission filter (Chroma, ET460/40M-2P) was placed before the

detector. Images (128 × 128 pixels; 184 × 184 μm2) were acquired with an integration time of

3 min while laser power (18.5 mW) and detector gain were kept constant. Time-resolved

fluorescence decay was analyzed by the phasor approach (32). Sine and cosine transforms

were applied to decay profiles at each pixel and the resulting phasors were plotted as a density

map. An instrument response function calibration was also performed to the resulting phasors

based on an umbelliferon standard. The centroid of the phasor was calculated for each group,

and a fitted line identified component lifetimes, assuming an underlying bi-exponential decay

profile.

3D epithelial stack acquisition

In vivo depth-resolved TPEF and SHG images (512 × 512 pixels; 200 × 200 μm) were

acquired using a MPTflex clinical tomograph at 790nm excitation as described previously

(23, 24). In brief, the TPEF signal was detected over 410nm-650nm, whereas SHG signal

was detected in the 385nm-405nm range. All in vivo measurements were conducted

according to an approved UC Irvine IRB protocol with written informed consent from all

participants. Optical sections were acquired at different depths with a 5-µm step with laser

power ranging between 5 mW close to the surface of the skin and up to 30 mW towards the

deeper skin layers. Twenty-nine stacks of tissue images from 14 participants (10 patients and

4 healthy volunteers) were incorporated in our study (1-4 healthy and/or diseased stacks of

tissue images per participant). More specifically, we acquired 12 healthy tissue stacks from 6

participants (2 patients and 4 healthy volunteers) and 17 diseased tissue stacks from 10

patients. In detail, the diseased tissue stacks comprised of 2 in situ melanoma stacks (2

patients), 7 invasive melanoma stacks (3 patients) and 8 basal cell carcinoma stacks (5

patients). Lesion sites were imaged prior to biopsy. A small sample (n = 3) of additional

TPEF tissue stacks were acquired from one of the in situ melanoma patients from locations

that appeared macroscopically suspicious (fig. S7A) but histologically exhibited solely dermal

inflammation (fig. S7A) and morphologically appeared normal in the TPEF optical sections

(fig. S7B). All lesions were diagnosed by a dermatopathologist (R.M.H), using standard

hematoxylin and eosin (H&E) histology or, Melan-A staining when necessary.

Image analysis of 3D epithelial stacks

Image processing was performed in ImJ and Matlab. Due to tissue complexity of the 3D

tissue stacks, a number of processing steps were required to isolate the cellular related

fluorescence. First, a binary mask was created from the SHG channel using Otsu’s

autothresholding to remove areas where collagen was recognized. This step helped to

eliminate from our analysis stromal cells, as the latter were expected to reside in the collagen

rich areas of the stroma. Further, chromophore separation (fig. S3) was performed using

Shanbhag’s entropy filtering (33). The selection of this specific thresholding algorithm was

based on the observation that pixels corresponding to fluorophores other than NADH created

clusters of higher intensity values. Therefore, they grouped in the image histogram around

neighboring gray levels. The thresholding level was universal for all optical sections included

in a tissue stack and the level was decided by the effect it had on planes within each stack

where no interfering chromophores were expected biologically. For example, a thresholding

level was considered accurate if within the stratum spinosum of the healthy epithelia where

no keratin, collagen or melanin associated fluorescence is expected, minimal or no pixels

were recognized as interfering chromophores.

In some tissue stacks, regions of interest (ROI) were also selected to avoid image

artifacts created by foldings of the epidermis. If a ROI had to be selected, it was universally

applied within the tissue stack and contained at least ~12-15 cells per frame. Lastly, as the

original tissue stacks included both the stratum corneum and significant depth information

from the dermis, a selection was made to define the depth range within which cellular related

analysis was to be performed. The first optical section below the stratum corneum where cells

clearly covered at least half of its area was selected as the top cellular layer. The same area

coverage criterion was used to select the bottom cellular layer with respect to the extracellular

contribution of the dermis. This normalized depth was typically in the range of 30 to 45 µm

for healthy tissues, whereas for diseased tissue stacks increased depths were noted, often

surpassing 100µm as a result of hyper-proliferation and/or significant dermal invasion. After

the above processing steps were completed, the nuclear and interstitial space removal, the

circular binary mask application and the depth-dependent computation of the mitochondrial

clustering biomarker were performed as described previously for the time-dependent

hypoxia–reperfusion measurements.

Immunohistochemistry

Healthy skin tissues discarded from cosmetic surgeries were collected according to an

approved UC Irvine IRB protocol with written informed consent from 3 participants. The

samples were fixed in 10% formalin and embedded in paraffin. Anti-DRP1 (HPA039324) and

Anti-hFis1 (HPA017430) Prestige Antibodies were purchased from Sigma-Aldrich.

Immunohistochemistry was performed by the Tufts Medical Center pathology services using

an automated staining machine (Benchmark Ultra, Ventana Medical Systems, Inc.) according

to standard practice protocol. The sections were counterstained with hematoxylin and a

bluing agent by using 3-3′-diaminobenzidine Detection Kit (Ventana Medical Systems, Inc.).

Control slides of human appendix were used to confirm the sensitivity and specificity of the

staining based on the Human Protein Atlas tissue protein expression localization scores. The

dilutions were 1:20 for the Anti-DRP1 antibody and 1:200 for Anti-FIS1 antibody. Digital

images of four random fields spread over the entire tissue section length (~6mm) were

acquired for each sample with a BZ-X710 Keyence microscope equipped with a 40× (NA

0.6) Nikon objective. Acquisition settings were held constant for all captured images per

antibody group. Visual, semi quantitative evaluation of the staining intensities within the

different epithelial layers was performed by the study’s board certified dermopathologist

(R.M.H), with expression levels of each protein being graded from 0 – 4 based on overall

staining intensity. True-color image analysis was also performed by using the Keyence BZ

image analysis software to quantify the staining intensities through the epithelial depth.

Proliferation, differentiation, and apoptosis studies

NHEKs were cultured on 35-mm collagen-coated glass-bottom dishes (Mattek). Basal

Keratinocyte Growth Medium (KGM Gold BulletKit, Lonza) containing low calcium was

used to keep NHEKs in the proliferating phase until reaching confluence. A subgroup of

NHEKs was then immediately imaged and comprised the proliferation/undifferentiated group.

To create the differentiating group, after reaching confluence, NHEKs were exposed to

differentiation media for at least 72 hours. The differentiation media was created by

supplementing basal, calcium-free medium (KGM-Gold without Ca+2, phenol red free

BulletKit, Lonza) with 2% fetal calf serum (FCS) and 1.8 mM Ca+2 (36). For the apoptotic

group, NHEKs were transfected with CellLight Mitochondria-RFP (Mito-RFP) upon seeding

and prior to differentiation according to the product’s online protocol. After differentiation,

the RFP expressing NHEKs cells were exposed to 5 µM of staurosporine for 4-6 hours.

Although little is known about the exact mechanisms of keratinization induction in-vivo,

keratinization is accepted to be a specialized form of apoptosis (37). Staurosporine was

chosen for the cell death induction, as it is known to be an effective apoptotic agent (35).

TMRE (20 nM) was the mitochondrial dye used to stain the proliferating and differentiating

NHEK groups, while the apoptotic group was stained with NucBlue Fixed Cell ReadyProbe

according to the suggested company protocol. TMRE was the mitochondrial dye of choice for

the proliferating and differentiating keratinocyte groups as it could serve a double purpose;

that was to reveal both the organization and healthy function of the mitochondrial

populations. TMRE is a potential dependent mitochondrial dye, hence accumulates only in

active, polarized mitochondrial. The choice of labeling in apoptotic group shifted to CellLight

Mitochondria-RFP (Mito-RFP) and NucBlue Fixed Cell ReadyProbe (NucBlue) for two

reasons. Firstly, NucBlue Fixed Cell was chosen as the apoptotic marker as it is a DAPI based

dye that is membrane impermeant and excluded from viable cells. Secondly, during apoptosis

mitochondria experience potential loss, therefore a labeling method that remains in the

mitochondria independent of their polarization was deemed more appropriate to accurately

stain the mitochondrial volume, hence the transfection with CellLight Mitochondria-RFP

(Mito-RFP). Incubation time for all dyes prior to imaging was 15 min at 37°C. Images were

acquired using a BZ-X710 Keyence fluorescence microscope equipped with a 60× (NA 1.4)

oil immersion Nikon objective.

Nuclear to cytoplasmic (N:C) ratio calculation

To calculate the nuclear to cytoplasmic (N:C) ratio for the 3D epithelial stacks, the

mean PSD depth dependent variance was calculated for each tissue stack. This

automated metric quantifies how the morphological features of specific size variate

over depth within a tissue stack. It does so by evaluating the depth dependent

variation of the spatial frequencies corresponding to the feature sizes of interest (fig.

S5C) In detail, to measure variation in cell morphology as a function of depth for our

tissue stacks, the cellular fluorescent features were used to fill any created image gaps

after the chromophore separation and the ROI selection, using a digital cloning

method described previously (28). Then the power spectral density (PSD) of every

image was computed (28). The variance over depth for each frequency within the

0.143-0.02 μm−1 PSD frequency range, corresponding to features between 7-50 μm,

was calculated from all optical sections for each tissue stack (29). The calculated PSD

variance was also normalized to the PSD variance at the highest spatial frequency

under the assumption that background image noise was consistent throughout the

entire tissue depth. The variances for the entire 0.143-0.02 μm−1 PSD frequency range

were then averaged, producing the mean PSD depth dependent variance for each

stack, which is correlated with the N:C ratio depth-dependent variations for that stack.

Supplementary Figures

Fig. S1. Automated image analysis steps before quantitative extraction of cellular-

related biomarkers. (A) General methodological steps followed for isolation of image

features attributed primarily to mitochondria and removal of dim edges. (B) Additional

processing steps that can be applied before the digital object cloning (DOC) and

mitochondrial organization calculation for intensity normalization within objects. (C) Final

NADH intensity image after the automated DOC algorithm has been applied to fill the voids

produced by the nucleic and interstitial feature removal. This image will be finally utilized for

the mitochondrial organization biomarker calculation.

Fig. S2. Setup for dynamic media oxygen saturation and live oxygen saturation

measurements. (A) Transverse and 3D perspective schematic view of the custom-built

construct designed to allow fast oxygen saturation manipulation of the cell media without

exposing cells to flow shear. The gas of choice (i.e nitrogen for hypoxia induction) is

delivered in the media via small gauge needles that face the perimetric edge of the well. The

system further allows dynamic media oxygen and temperature measurements through

respective oxygen and temperature probes. (B) Oxygen measurements from the hypoxia-

reperfusion dynamic 2D experiments. The oxygen was measured dynamically during the

imaging session only for the keratinocytes exposed to hypoxic treatment. Values are

presented as means ± standard error of the mean (S.E.M.), N = 4 for the hypoxic group. As

the normoxic keratinocyte group was placed during the imaging session in a commercial

micro-incubator at physiological conditions (37 °C and 5% CO2), so the oxygen saturation

levels for that group were considered to be stable at atmospheric levels, and this is

represented by the stable normoxia line shown.

Fig. S3. Automated digital filter–based identification of endogenous TPEF

contributions from non-NADH chromophores. Raw TPEF images (top panels) and

corresponding chromophore segmentation masks (bottom panels) resulting from the

Shanbhag entropy filtering are shown for a variety of tissue stacks. The image areas presented

as black in the segmentation masks are the areas considered as chromophores other than

NADH and are therefore discarded from the cellular analysis. Respective Melan A staining

from the invasive melanoma lesion (lowest panel) is also presented, with melanin stained red.

Fig. S4. Detailed mitochondrial clustering profiles. (A) Mitochondrial clustering values

from a finely sampled healthy epidermis. The tissue stack was acquired with a 1 µm step from

a healthy volunteer (shown also in Fig. 4 as Healthy 2). The extracted mitochondrial

clustering values for each optical section are shown as dots. The penalized spline fit with λ =

0.005 is shown as a solid line. (B) Full stack mitochondrial clustering values for a

representative basal cell carcinoma and invasive melanoma. Blue shaded region shows the

dermal depths invaded by a cellular nest in the BCC lesion (left panel). Data from these

samples are included in Fig. 4 only up to a depth of 50 m to maintain consistency in the x-

axis scale for all traces included in that figure.

Fig. S5. The depth-dependent mean PSD variance is correlated with the

corresponding N/C ratio (nuclear-to-cytoplasmic ratio) variation. (A) Representative

TPEF images from distinct layers of a normal stratified epithelium illustrating that the cell and

nucleus sizes vary as a function of depth. (B) Averages and standard deviations for cell and

nuclear sizes for each cellular layer, extracted from 5 randomly selected cells within each layer.

(C) Representative depth-dependent PSD variance from a Healthy and an Invasive Melanoma

tissue stack. The shaded region identifies the spatial frequency range that describes features

between 50 and 7 m.

Fig. S6. Organization patterns in stained mitochondria images of 2D cultures of

undifferentiated, differentiated, and apoptotic cells. Representative stained primary

human epidermal keratinocytes in undifferentiated (proliferating), differentiated or apoptotic

phase. TMRE fluorescence is shown for the proliferating and differentiated keratinocytes,

while NucBlue Fixed Cell ReadyProbe (NucBlue) and Cell-Light RFP (Mito-RFP)

fluorescence are shown for the apoptotic group. Scale bar is 10 m for all images.

Fig. S7. Mitochondrial organization distinguishes benign from malignant

macroscopically suspicious sites. (A) Macroscopic image from macroscopically

suspicious lesions located in the participant’s left arm, with circles designating the locations

from where TPEF image stacks were captured. Biopsies were acquired after imaging only

from the numbered locations (1-3). Histopathological H&E 20× images from the numbered

locations (1-3) and their corresponding diagnosis are also shown. White arrows within

location1 H&E image designate the presence of melanocytes in upper epithelial layers,

consistent with an amelanotic melanoma lesions. Only dermal inflammation was detected in

the sections from locations 2 and 3 without any other intraepithelial pathology. Scale bar is 50

µm for all H&E images. (B) Raw TPEF images from different depths of the respective tissue

sites presented in (A). The corresponding mitochondrial clustering values as a function of

depth extracted from those tissue stacks are presented at the most right. The A,B,C,D labels

within the mitochondrial clustering panels denotes the mitochondrial clustering values

extracted from the panel’s respective A,B,C,D labeled images. Yellow arrows in the top most

panel show dendritic cells (melanocytes) appearing within the upper epidermal layers. No

morphologically suspicious cells were observed within the optical stacks representing areas of

dermal inflammation. TPEF images and clustering trends corresponding to the normal

location (green circle in (A)) are presented in Fig. 4 as Healthy 1. Scale bar is 50 m for all

images.