Skip to content
#protein folding Open access

The Mystery of the 10⁵ Temperature Gap Inside Cells: The Limits and Challenges of Nanothermometry

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
thermodynamics and calorimetric analyses

Abstract

Researchers at Osaka University in Japan and the University of Queensland in Australia have published a review analysing the enormous gap between theory and measured values that confronts luminescence nanothermometry, the technique used to measure temperature inside cells. The authors argue that the concept of temperature itself remains valid in statistical-thermodynamic terms even at the 10 nm scale, but report that the so-called "10⁵ gap issue", in which measured values (~1 K) run 100,000 times larger than calculated ones (~10 μK), remains unresolved. Attempts have been made to narrow the gap by assigning a lower thermal conductivity to intracellular membranes and by taking Kapitza resistance into account, but the authors call for more refined measurement methods alongside a theoretical rethink before the phenomenon can be fully explained. [Quantum Biology Society] Pinning down quantitatively when, where and how much heat is generated at the cellular level is a central problem in understanding how organisms maintain body temperature and run their metabolism. As luminescence nanothermometry has advanced, using fluorescent proteins, quantum dots and nanodiamonds among other probes, striking results have been reported: stable temperature differences of more than 1 K between organelles even in unstimulated cells, and mitochondrial temperatures that may possibly rise as high as 323 K (about 50 °C) under full activation of respiration in human embryonic kidney 293 cells and primary skin fibroblasts. A review by Madoka Suzuki of Osaka University and Taras Plakhotnik of the University of Queensland, published in Biophysical Reviews in 2020, takes on the fundamental dilemma sitting behind those spectacular observations. ■ Is the Concept of Temperature Valid at the Nanoscale? Before weighing the reliability of nanothermometers, the authors first examine whether temperature, a macroscopic state function, can even be defined in the microscopic world of the nanometre scale. Statistical mechanics says that the smaller the system, the more severe its temperature fluctuations become. Molecular dynamics simulations put the temperature fluctuation of a single amino acid residue at around 70 K, and the textbook formula gives the same figure for a spherical volume of water with a radius of 0.25 nm. But the characteristic correlation time of these fluctuations is extremely short: roughly 15 ps for a spherical region of radius 1.5 nm in water, and about 12 ns for a nanodiamond of radius 50 nm. At the 10 nm scale the fluctuations run on the order of 1 K, with a characteristic time on the order of 0.1 ns. Since real measurement times are far longer than this, random temperature fluctuations average out. The authors put a number on it: the thermodynamically limited noise floor for a 50 nm thermometer is a few μK s^1/2, more than three orders of magnitude below the best experimental figure achieved so far. The upshot is that in aqueous conditions and with luminescent temperature probes, the concept of temperature holds even at the 10 nm scale, and thermal fluctuation is not what limits present-day nanothermometry. ■ The Heart of the Contradiction: The 10⁵ Gap Issue The hardest problem in this field is the gap between calculation and measurement that refuses to close. The calculation: when cells are assumed to have the thermal conductivity of water and the heat equation is applied, the rise in whole-cell temperature from a local heat source comes out very small. Suzuki's own group calculated that whole-cell temperature in HeLa cells could rise by only 10 μK (0.00001 K) if the sarco/endoplasmic reticulum Ca²⁺-ATPase (Serca) were solely responsible for the temperature changes measured on Ca²⁺ shock. The measurement: Yang and colleagues measured a local temperature rise of about 1 K in the NIH3T3 cell line using quantum-dot nanothermometry. Yet accounting for that rise theoretically would require a heat source of about 1 μW or more, a figure that appears to be three orders of magnitude larger than what has been determined in stimulated brown adipocytes, cells known for generating heat. The name for this 100,000-fold (10⁵) discrepancy comes from a 2015 paper by Suzuki's group, the review's first author, and it has been the subject of fierce debate ever since, running through a published exchange between Baffou's group, which set out the critique, and researchers working with fluorescent thermometers. ■ Cross-Checking With Non-Luminescent Probes, and Rethinking Thermal Conductivity Could the fluorescent thermometers be responding to intracellular variables other than temperature, such as viscosity, pH or ionic strength, and producing an artefact? Cross-checks with non-luminescent probes: the authors point to significant temperature rises observed with probes working on entirely different principles. Bimetal microcantilevers registered about 0.2 K in stimulated brown adipocytes. Micro-thermocouple arrays inside a thermally stabilised system detected frequent fluctuations of about 60 mK and, in one detection area, a continuous elevation of up to 285 mK, while other areas stayed stable. A microscale thermocouple probe detected rapid rises of about 7.5 K near mitochondria in neurons of the sea slug Aplysia californica when the cells were stimulated with a proton uncoupler. Given this variety of probes and methods, the authors conclude that it may be unnecessary to decide that the temperature increase is unmeasurable in individual cells. Thermal conductivity and Kapitza resistance: one attempt to narrow the gap has been to question the thermal conductivity used in the calculations. Bastos and colleagues measured the thermal conductivity of a single lipid bilayer experimentally at about 0.2 W m⁻¹ K⁻¹ at 300 K, only a third that of water. Bringing in Kapitza resistance, the thermal resistance at the boundary between two different materials, the authors' modelling brings the average effective thermal conductivity inside a cell down to around 0.1 W m⁻¹ K⁻¹, roughly six times smaller than water. ■ Significance and Open Questions Resetting thermal conductivity to a lower value to reflect the complexity of the cell's interior does close some of the distance between calculation and measurement, but nowhere near enough to fill a 100,000-fold gap. The authors put it plainly: the gap still remains although it has narrowed, and they suggest this may give some ground for optimism about eventually closing it. The review settles that the concept of temperature stays physically valid down to the 10 nm scale, while summing up coolly the divide between theory and experiment that the field now faces. Temperature is more than an index of heat. It shifts chemical equilibria, affects flows driven by electrochemical gradients, and has been shown to drive directional motion of particles and to induce the accumulation of nucleotides and lipids, which makes it a core variable in the metabolism of living things. The authors conclude that resolving the 10⁵ dilemma will require approaching the gap from both sides at once: refining the theoretical estimates as the complexity of cellular processes becomes better understood, and developing new measurement methods that are more accurate and less susceptible to artefacts. #IntracellularTemperature #Nanothermometry #LuminescenceNanothermometry #FiveOrdersGap #ThermalConductivity #KapitzaResistance #ThermalFluctuations #StatisticalMechanics #Nanodiamond #ODMR #Thermogenesis #Mitochondria #Review #QuantumBiology #BiophysicalReviews Source (Biophysical Reviews, open access): https://doi.org/10.1007/s12551-020-00683-8 Commentary from the sceptical side (How hot are single cells?, free): https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398142/ Perspective defending the measurements (2021, free): https://pmc.ncbi.nlm.nih.gov/articles/PMC8660847/

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

In the context of cloud computing, risks associated with underlying technologies, risks involving service models and outsourcing, and enterprise readiness have been recognized as potential barriers for the adoption. To accelerate cloud adoption, the concrete barriers negatively influencing the adoption decision need to be identified. Our study aims at understanding the impact of technical and security-related barriers on the organizational decision to adopt the cloud. We analyzed data collected through a web survey of 352 individuals working for enterprises consisting of decision makers as well as employees from other levels within an organization. The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability. The result from our logistic regression analysis confirms the criticality of the security concern, which results in an up to 26-fold increase in the non-adoption likelihood. Our study underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

To compete in this age of disruption, large companies cannot rely on cost efficiency, lead time reduction and quality improvement. They are now looking for ways to innovate like startups. Meanwhile, the awareness and use of the Lean startup approach have grown rapidly amongst the software startup community in recent years. This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors. A multiple case study approach is followed in the investigation. Two software product innovation projects from two large companies are examined, using a conceptual framework that is based on the method-in-action framework and extended with the previously developed Lean-Internal Corporate Venture model. Seven face-to-face in-depth interviews of the employees with different roles are conducted. Within-case analysis and cross-case comparison are applied to draw the findings from the cases. A generic process flow summarises the common key processes of Lean internal startups. The findings suggest that an internal startup that is initiated management or employees faces different challenges. A list of enablers of applying Lean startup in large companies are identified, including top management support and cross-functional team. Both cases face different inhibitors due to the different process of inception, objective of the team and type of the product. Our contributions are threefold. First, this study is one of the first attempt to investigate the use of Lean startup approach in large companies empirically. Second, the study shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context. The third is a general process of Lean internal startup and the evidence of the enablers and inhibitors of implementing it, which are both theory-informed and empirically grounded.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

Affects--emotions and moods--have an impact on cognitive processing activities and the working performance of individuals. It has been established that software development tasks are undertaken through cognitive processing activities. Therefore, we have proposed to employ psychology theory and measurements in software engineering (SE) research. We have called it "psychoempirical software engineering". However, we found out that existing SE research has often fallen into misconceptions about the affect of developers, lacking in background theory and how to successfully employ psychological measurements in studies. The contribution of this paper is threefold. (1) It highlights the challenges to conduct proper affect-related studies with psychology; (2) it provides a comprehensive literature review in affect theory; and (3) it proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#human-computer interacti... Open access May 2025

Linker-free PROTACs efficiently induce the degradation of oncoproteins

Proteolysis-targeting chimeras (PROTACs) present a potentially effective strategy against various diseases via selective proteolysis. How to increase the efficacy of PROTACs remains challenging. Here, we explore the necessity of the linker, which has been deemed as an integral part of heterobifunctional PROTACs. Adopting single amino acid-based degradation signals, we find that the linker is not a required feature of the PROTACs. Notably, the linker-free PROTAC, Pro-BA, exhibits superior efficacy over its linker-bearing counterparts in degrading EML4-ALK and inhibiting lung cancer cell growth, as Pro-BA induces a stronger interaction between the target and the E3 ubiquitin ligase. Pro-BA is a water-soluble, orally administered degrader that significantly inhibits the tumor growth in a xenograft mouse model. The broad applicability of this linker-free PROTAC strategy is further validated through the development of BCR-ABL degrader. Our study introduces a design paradigm for PROTACs, potentially facilitating the advancement of more efficient therapeutic degraders. Linkers are traditionally seen as important for PROTAC activity. Here, the authors demonstrate that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.

Jianchao Zhang, Congli Chen, Xiao Chen et al. · 41 citations
#machine learning Open access Nov 2025

mRNABERT: advancing mRNA sequence design with a universal language model and comprehensive dataset

Designing effective mRNA sequences for therapeutics remains a formidable challenge. Inspired by successes in protein design, language models (LMs) are now being applied to RNA, but progress is often impeded by the lack of comprehensive training data. Existing models are frequently limited to UTR or CDS regions, restricting their application for complete mRNA sequences. We introduce mRNABERT, a robust, all-in-one mRNA designer pre-trained on the largest available mRNA dataset. To enhance performance, we propose a dual tokenization scheme with a cross-modality contrastive learning framework to integrate semantic information from protein sequences. On a comprehensive benchmark, mRNABERT demonstrates state-of-the-art performance, outperforming previous models in the majority of tasks for 5’ UTR and CDS design, RNA-binding protein (RBP) site prediction, and full-length mRNA property prediction. It also surpasses large protein models in several related tasks. In conclusion, mRNABERT’s superior performance across these diverse tasks signifies a substantial leap forward in mRNA research and therapeutic development. Designing complete mRNA sequences for new vaccines and therapies is a complex challenge. Here, the authors develop mRNABERT, a foundational AI model that designs entire mRNA sequences and demonstrates superior performance across comprehensive benchmarks.

Ying Xiong, Aowen Wang, Yu Kang et al. · 22 citations · ⚡1

Related blog posts

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

Google DeepMind Blog Nov 25, 2025

AlphaFold: Five years of impact

Explore how AlphaFold has accelerated science and fueled a global wave of biological discovery.