Cell stress panel

The cell stress panel is a combination of endpoints that delivers a toxicity profile for novel therapeutics providing evidence of the toxicological mode of action. It consists of 36 biomarkers that represent cellular stress signalling pathways, organelle health and cellular cytotoxicity.

Cyprotex deliver consistent, high quality data with the flexibility to adapt protocols based on specific customer requirements.

Background Information

Current in vitro preclinical strategies require ethical and regulatory consideration of animal use (3R's) and require human relevant data. This expedites new approach methodologies (NAMs) aimed at accurately predicting in vivo toxicities at therapeutically relevant in vivo concentrations ^1,2.
Despite in vitro two-dimensional cellular assays lacking the complexity of the in vivo microenvironment, they provide a fast, economical readout for high throughput analysis ^3,4.
36 biomarkers are measured (figure 1) that represent 12 key cellular stress mechanisms including mitochondrial toxicity, gene regulation and inflammation. In addition, general cell health are monitored utilising a variety of in vitro assays including the Seahorse platform, Promega kits and ELISAs, however, predominantly high content imaging (HCI) is used.
It has been shown that the cellular stress panel can be used, together with other new approach methodologies, to identify chemical exposures that are protective of consumer health ⁵.

Next Generation Risk Assessment (NGRA) is defined as an exposure-led, hypothesis driven risk assessment approach that integrates in silico, in chemico and in vitro approaches.

¹Dent M et al., (2018) Comput toxicol 7; 20-26

Protocol

Cell stress panel protocol

Cell Line	HepG2 (human hepatoblastoma) cell line*
Analysis Platform	Confocal Cellomics ArrayScan^® (Thermo Scientific) Seahorse XF^e96 extracellular flux analyser Intellicyt iQue^® Screener PLUS.
Test Compound Concentrations	8 point dose response curve with top concentration based on 100x C_max or solubility limit. 3 replicates per concentration.*
Compound Requirements	200 μL of a solution to achieve 100x C_max (200 x top concentration to maintain 0.5% DMSO) or equivalent amount in solid compound.
Time Points	24 hour*
Quality Controls	Negative control: 0.5% DMSO (vehicle) Positive control: 2 compounds per mechanism
Data Delivery	Dose response curve, MEC and AC₅₀ value for each measured parameter. Cell count, nuclear size, DNA structure, ER stress panel 1 (ER integrity, BiP, XBP1), ER stress panel 2 (ATF4, PERK, CHOP), AhR translocation, DNA damage (pH2AX, p53), inflammation & pH (ICAM-1, intracellular pH, HIF1α, IL-1β, IL-6, IL-8, IFN-γ), mitochondrial oxidative stress (mitochondrial ROS, PGC1α, TNFAIP3), oxidative stress (NRF2, HMOX1, SRXN1), osmotic & heat shock (NFAT, hsp70), metal stress (metallothionein, MTF1), apoptosis & necrosis (caspase 3/7, NFkB, cell membrane permeability), phospholipidosis & steatosis, glutathione (GSH) content, reactive oxygen species (ROS), mitochondrial membrane potential, mitochondrial mass, cellular ATP & LDH release, Seahorse assay (oxygen consumption rate (OCR), extracellular acidification rate (ECAR), reserve capacity)*

* other options available on request.

Figure 1
Cell stress panel overview summarising the 12 cell stress mechanisms and their associated molecular markers detected within the cell stress panel assay.

Data

Data from Cyprotex's Cell Stress Panel

Figure 2
Representative HCS images of the endoplasmic reticulum (ER) stress panel assay showing HepG2 cells labelled with Hoechst (blue, nuclei), ER tracker (green, an ER integrity dye), BiP (orange, an ER chaperone with high affinity for misfolded proteins) and CHOP (magenta, a transcription factor activated via PERK) following 24 hour exposure to 10 µM of known ER toxin, tunicamycin.

Figure 3
Representative dose response curves of the endoplasmic reticulum (ER) stress panel assay showing (a) ER integrity, (b) BiP and (c) CHOP in HepG2 cells following 24 hour exposure to known ER toxin, tunicamycin.

An increase in endoplasmic reticulum (ER) integrity staining (figure 2 & 3a) suggests increased ER dyshomeostasis with a higher abundance of unfolded and/or misfolded proteins requiring BiP (figure 3b) binding and thus increased CHOP activation (figure 3c) following PERK receptor targeting by BiP bound proteins.

Figure 4
Representative HCS images of the mitochondrial oxidative stress panel assay showing HepG2 cells labelled with Hoecst (blue, nuclei), MitoSox (orange, mitochondrial ROS), PGC1α (red, transcription factor co-activator in mitochondrial biogenesis and metabolic homeostasis), and TNFAIP3 (green, cytoplasmic protein regulator involved in inflammation and immunity) following 24 hour exposure to 0.1 and 40 µM of a known mitochondrial toxin, rotenone.

Figure 5
Representative dose response curves of the mitochondrial oxidative stress panel showing (a) mitochondrial ROS formation, (b) PGC1α activation and expression and (c) TNFAIP3 expression in HepG2 cells following 24 hour exposure to known mitochondrial toxin, rotenone.

An increase in mitochondrial ROS (reactive oxygen species) (figure 5a) activates PGC1α (figure 5b) which signals the increased transcription of TNFAIP3 (figure 5c) and the induction of an inflammatory response while also activating a redox pathway.

Figure 6
Representative HCS imaging of the metal stress assay showing HepG2 cells labelled with Hoechst (blue, nuclei) and metallothionein (green, a protein induced by transcription factor MTF-1) following exposure to 4 µM of known metal toxicant, cadmium chloride. (b) Representative dose response curve showing the cadmium chloride induced metallothionein response in HepG2 cells following 24 hour exposure.

Metallothionein protein is regulated by MTF1 in order to bind and sequester toxic heavy metals, therefore an increase in this molecular marker indicates metal stress (figure 6a & 6b).

Hatherell et al (2020) describe the initial validation of the cell stress panel whereby this predominately high content imaging (HCI) strategy has the potential to improve our understanding of chemical exposure outcomes using point of departure (PoD) in relation to C_max with a set of 13 benchmark substances⁵.

In combination with other cellular assays and in silico approaches this panel could provide a powerful NGRA tool to use in non-animal safety decision making.

References

¹ Dent M et al. (2018) Principles underpinning the use of new methodologies in the risk assessment of cosmetic ingredients. Comput Toxicol 7: 20-26
² Middleton A et al (2017) Case studies in cellular stress: defining adversity/adaptation tipping points. Appl In Vitro Toxicol 3(2): 199-210
³ Campbell JL et al. (2012) Physiologically based pharmacokinetic/toxicokinetic modeling. Methods Mol Biol 929: 439-499
⁴ Moxon TE et al. (2020) Application of phisiologically based kinetic (PBK) modelling in the next generation risk assessment of dermally applied consumer products. Toxicol In Vitro 63: 104746
⁵ Hatherell S et al. (2020) Identifying and characterizing stress pathways of concern for customer safety in next generation risk assessment. Toxicol Sci 10.1093/toxsci/kfaa054