Sohum Mehta’s research while affiliated with University of California System and other places

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Publications (83)


Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals
  • Literature Review
  • Full-text available

November 2024

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64 Reads

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1 Citation

Chemical Reviews

Anneliese M M Gest

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Ayse Z Sahan

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[...]

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Jin Zhang

Cellular function is controlled through intricate networks of signals, which lead to the myriad pathways governing cell fate. Fluorescent biosensors have enabled the study of these signaling pathways in living systems across temporal and spatial scales. Over the years there has been an explosion in the number of fluorescent biosensors, as they have become available for numerous targets, utilized across spectral space, and suited for various imaging techniques. To guide users through this extensive biosensor landscape, we discuss critical aspects of fluorescent proteins for consideration in biosensor development, smart tagging strategies, and the historical and recent biosensors of various types, grouped by target, and with a focus on the design and recent applications of these sensors in living systems.

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Development and characterization of ExRai-CKAR2
a, Top, modulation of cpEGFP fluorescence by a molecular switch dependent on PKC-mediated phosphorylation. Bottom, domain structure of ExRai-CKAR2. b, Representative in vitro ExRai-CKAR2 fluorescence spectra collected at 530-nm emission (i) and 405-nm (ii) or 480-nm excitation (iii) without (black traces) or with (green traces) ATP, Ca²⁺ and lipids in the presence of purified PKCα (n = 3 independent experiments). AU, arbitrary units. c, Representative images of ExRai-CKAR2 fluorescence in Cos7 cells at 480-nm (Ex480, top) and 405-nm (Ex405, middle) excitation. Bottom, pseudocolored images of the change in excitation ratio upon PMA stimulation and Gö6983 inhibition. Warmer colors indicate higher ratios. The solid arrowhead indicates PMA addition and the hollow arrowhead indicates Gö6983 addition. Images are representative of three independent experiments. Scale bar, 10 μm. d, Representative average time courses showing the 480/405 excitation ratio responses of wild-type (WT) ExRai-CKAR2 and a nonphosphorylatable ExRai-CKAR2 T/A mutant in Cos7 cells treated with PMA and Gö6983. e, Representative average time courses comparing the 480/405 excitation ratio or Y/C emission ratio responses of ExRai-CKAR2, ExRai-CKAR1 or CKAR2 in Cos7 cells treated with 50 ng ml⁻¹ PMA. f, Quantification of the maximum PMA-stimulated response of each biosensor (n = 35, n = 27, n = 20 and n = 22 cells from three independent experiments each). Time courses in d,e are representative of three independent experiments; solid lines indicate mean responses and shaded areas indicate the s.e.m. Data in f were analyzed using ordinary one-way ANOVA followed by Dunnett’s multiple-comparison test. ****P < 0.0001. Data are the mean ± s.e.m.
Source data
Subcellular targeting of ExRai-CKAR2
a–d, Domain structures (top), average 480/405 excitation ratio time courses (bottom left), quantification of maximum response (bottom middle) and representative images (bottom right) of ExRai-CKAR2 and ExRai-CKAR2 T/A negative control targeted to the cytoplasm (a; WT, n = 27 cells; T/A, n = 30 cells), PM (b; WT, n = 27 cells; T/A, n = 30 cells), ER (c; WT, n = 39 cells; T/A, n = 30 cells) and lysosomes (d, WT, n = 33 cells; T/A, n = 30 cells) in Cos7 cells stimulated with 50 ng ml⁻¹ PMA. Scale bars, 10 μm. Time courses are representative of three independent experiments; solid lines indicate mean responses and shaded areas indicate the s.e.m. Statistical analyses were performed using a two-sided Student’s t-test. ****P < 0.0001. Data are the mean ± s.e.m.
Source data
PKC regulation at the ER and lysosome
a, Representative average 480/405 excitation ratio time courses (hereafter, ‘time courses’) from UTP (100 μM)-stimulated Cos7 cells expressing PM-ExRai-CKAR2 (n = 10 cells), ER-ExRai-CKAR2 (n = 15 cells) or Lyso-ExRai-CKAR2 (n = 16 cells). Maximum responses (ΔR/R0) and SAM15 quantified from n = 25, n = 36 and n = 36 cells. b,c, ER-ExRai-CKAR2 response time courses from Cos7 cells without (Ctrl, n = 10 cells) or with (Gö6976, n = 12 cells) 30-min Gö6976 preincubation or coexpressing PKCα-mCherry (PKCα, n = 14 cells) stimulated with UTP (b) or with 5 μM B106 followed by UTP (c; n = 15 cells). d, Maximum responses quantified from n = 36 (Ctrl), n = 34 (Gö6976), n = 23 (B106) and n = 32 (PKCα) cells. e, ER-ExRai-CKAR2 response time courses from UTP-stimulated Cos7 cells cotransfected with control siRNA (scramble, n = 18 cells), PKCα siRNA (n = 15 cells) or PKCβ siRNA (n = 9 cells). Maximum responses quantified from n = 28 (siRNA-ctrl), n = 24 (siRNAα) and n = 19 (siRNAβ) cells. f,g, Lyso-ExRai-CKAR2 response time courses from Cos7 cells pretreated with Gö6976 (1 μM) followed by UTP (f; n = 16 cells) or without (Ctrl, n = 12 cells) or with 30-min B106 pretreatment (B106, n = 9 cells) or coexpressing PKCδ-mCherry (g; PKCδ, n = 12 cells). h, Maximum responses quantified from n = 36 (Ctrl), n = 37 (Gö6976), n = 21 (B106) and n = 28 (PKCδ) cells. i, Lyso-ExRai-CKAR2 response time courses from UTP-stimulated Cos7 cells cotransfected with control siRNA (scramble, n = 16 cells) or PKCδ siRNA (n = 12 cells). Maximum responses quantified from n = 29 (siRNA-ctrl), n = 19 (siRNAδ) and n = 24 (B106) cells. Time courses are representative of three independent experiments; solid lines indicate mean responses and shaded areas indicate the s.e.m. Maximum responses and SAM15 levels were quantified from three independent experiments; data are the mean ± s.e.m. ****P < 0.0001. Data were analyzed using ordinary one-way ANOVA followed by Dunnett’s multiple-comparison test.
Source data
Lysosomal DAG is critical for lysosomal PKC activity
a, Detection and depletion of lysosomal DAG by Lyso-Digda and Lyso-DGK, respectively. Raw Y/C emission ratios from Lyso-Digda-expressing Cos7 cells with or without PDBu stimulation or Lyso-DGK coexpression (n = 24 cells each from three independent experiments; P = 0.0028, −Lyso-DGK/−PDBu versus −Lyso-DGK/+PDBu; P = 0.0004, −Lyso-DGK/−PDBu versus +Lyso-DGK/−PDBu). b, Representative average 480/405 excitation ratios time courses (hereafter, ‘time courses’) in Cos7 cells expressing Lyso-ExRai-CKAR2 (WT, n = 18 cells), Lyso-ExRai-CKAR2 T/A (T/A, n = 16 cells) or Lyso-ExRai-CKAR2 and PKCδ (+PKCδ, n = 12 cells) treated with 5 μΜ B106. c, Lyso-ExRai-CKAR2 response time courses in Cos7 cells treated with B106 (B106, n = 18 cells) or 1 μM Gö6976 (Gö6976, n = 14 cells). d, Maximum responses quantified from n = 38 (WT), n = 22 (WT + PKCδ), n = 35 (T/A) and n = 34 (Gö6976) cells. e, Lyso-ExRai-CKAR2 response time courses from UTP (100 μΜ)-stimulated Cos7 cells without (−Lyso-DGK, n = 17 cells) or with (+Lyso-DGK, n = 10 cells) Lyso-DGK coexpression. Maximum responses quantified from n = 36 and n = 22 cells. f, Lyso-ExRai-CKAR2 response time courses from Cos7 cells without (−Lyso-DGK, n = 18 cells) or with Lyso-DGK (+Lyso-DGK, n = 20 cells) coexpression and treated with B106. Maximum responses quantified from n = 38 and n = 44 cells. g, Lyso-ExRai-CKAR2 response time courses in TG-stimulated Cos7 cells without (−Lyso-DGK, n = 10 cells) or with Lyso-DGK (+Lyso-DGK, n = 9 cells) coexpression or with 30-min Gö6976 (Gö6976, n = 11 cells) pretreatment (TG). Maximum responses quantified from n = 30 (−Lyso-DGK), n = 23 (+Lyso-DGK) and n = 24 (Gö6976) cells. Time courses are representative of three independent experiments; solid lines indicate mean responses and shaded areas indicate the s.e.m. Quantifications are from three independent experiments; data show the mean ± s.e.m. ****P < 0.0001. Data were analyzed using ordinary one-way ANOVA followed by Tukey’s multiple-comparisons test (a) or Dunnett’s multiple-comparison test (c,d,g) or using a two-sided Student’s t-test (e,f).
Source data
ExRai-CKAR2 reports endogenous aPKC activity in organoids
a, Representative average 480/405 excitation ratio time courses (hereafter, ‘time courses’) or Y/C emission ratio responses in HeLa cells expressing ExRai-CKAR2 (n = 24 cells) or CKAR2 (n = 20 cells), respectively, treated with 5 μM pz09. b, Response time courses from HeLa cells coexpressing ExRai-CKAR2 (WT + PKM, n = 24 cells) or ExRai-CKAR2 T/A (T/A + PKM, n = 28 cells), along with mCherry-tagged constitutively active PKMζ, and treated with 5 μM pz09. c, Maximum responses quantified from n = 20, n = 24, n = 24 and n = 28 cells. d, Representative fluorescence and pseudocolor images of 3D-cultured MDCK organoids stably expressing Cyto-ExRai-CKAR2. The pseudocolor images depict raw Cyto-ExRai-CKAR2 excitation ratios (Ex480/405); warmer colors indicate higher ratios. Scale bar, 20 μm. e, Response time courses from 3D-cultured MDCK organoids expressing Cyto-ExRai-CKAR2 (WT, n = 38 cells) or Cyto-ExRai-CKAR2 T/A (T/A, n = 40 cells) and treated with pz09. Right, maximum responses quantified from n = 38 and n = 40 cells in four organoids. Time courses are representative of three independent experiments; solid lines indicate mean responses and shaded areas indicate the s.e.m. Quantifications are from three independent experiments; data show the mean ± s.e.m. Data were analyzed using ordinary one-way ANOVA followed by Tukey’s multiple-comparisons test (c) or a two-sided Student’s t-test (e). ****P < 0.0001.
Source data

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Sensitive fluorescent biosensor reveals differential subcellular regulation of PKC

October 2024

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31 Reads

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2 Citations

Nature Chemical Biology

The protein kinase C (PKC) family of serine and threonine kinases, consisting of three distinctly regulated subfamilies, has been established as critical for various cellular functions. However, how PKC enzymes are regulated at different subcellular locations, particularly at emerging signaling hubs, is unclear. Here we present a sensitive excitation ratiometric C kinase activity reporter (ExRai-CKAR2) that enables the detection of minute changes (equivalent to 0.2% of maximum stimulation) in subcellular PKC activity. Using ExRai-CKAR2 with an enhanced diacylglycerol (DAG) biosensor, we uncover that G-protein-coupled receptor stimulation triggers sustained PKC activity at the endoplasmic reticulum and lysosomes, differentially mediated by Ca²⁺-sensitive conventional PKC and DAG-sensitive novel PKC, respectively. The high sensitivity of ExRai-CKAR2, targeted to either the cytosol or partitioning defective complexes, further enabled us to detect previously inaccessible endogenous atypical PKC activity in three-dimensional organoids. Taken together, ExRai-CKAR2 is a powerful tool for interrogating PKC regulation in response to physiological stimuli.


KINACT for cumulative PKA activity recording in live cells
a Schematic of PKA activity integrator and diagram of light- and kinase activity-induced gene expression. b The domain structures of A-KINACT (1), A-KINACT (T/A, 1’) and reporter (2). c Snapshot imaging of cumulative PKA activity in A-KINACT and A-KINACT (T/A) dual-stable cells under five treatment conditions. d, Statistical quantification of total EGFP intensity under all conditions. *P = 0.0388 (T/A, +F/I/+light) and ****P = 9.99 × 10⁻¹⁶ (A-KINACT, +F/I/+light). e The fraction and mean EGFP/mCherry (G/R) intensity ratio of H2B-EGFP⁺ cells stably expressing A-KINACT. n = 52, n = 73, n = 285, n = 1975 and n = 154 cells. f The fraction and G/R ratio of H2B-EGFP⁺ cells stably expressing A-KINACT (T/A). n = 52, n = 47, n = 37, n = 13 and n = 36 cells. g The quantification of 3 independent Iso dose-response experiments in A-KINACT cells. The red lines represent 3 fitted logistic curves with an average fit value for the EC50. h The quantification of light-gated experiments with full-time drug treatment (F/I, 50/100 μM or Iso, 100 nM) but 5 min time window of light illumination. Data points correspond to start of illumination (min after drug addition). *P = 0.0417 (Iso, 5–10 min), *P = 0.0333 (Iso, 10–15 min), ****P = 7.08 × 10⁻⁵ (F/I, 0–5 min), ****P = 1.33 × 10⁻⁷ (F/I, 5–10 min), ****P = 1.82 × 10⁻¹³ (F/I, 10–15 min) and ****P = 7.46 × 10⁻¹⁰ (F/I, 15–20 min). i Domain structure of outer mitochondrial membrane-targeted A-KINACT (Mito-A-KINACT) system. DAKAP motif was tethered to the N-terminus of A-KINACT (1). j Snapshot imaging of cumulative PKA activity near mitochondria under four treatment conditions. k Statistical quantification of total EGFP intensity under all conditions. **P = 0.0094 (Mito-A-KINACT, -F/I/ + light), ****P = 3.88 × 10⁻⁶ (Mito-A-KINACT, +F/I/ + light), **P = 0.0042 (T/A, -F/I/ + light) and **P = 0.0041 (T/A, +F/I/ + light). For (d–f) data from 4 independent experiments. For (g, h) and (k) data from 3 independent experiments. For (c) and (j) scale bars, 10  μm. For (d–h) and (k) statistical analysis was performed using ordinary one-way ANOVA followed by Dunnett’s multiple-comparisons test. NS, not significant. Data are mean ± s.e.m. Source data are provided as a Source Data file.
General applicability of KINACT to different types of kinases
a Domain structures of C-KINACT and C-KINACT (T/A). b Representative images showing C-KINACT and mutant C-KINACT (T/A) reporting PKC activity changes induced by PDBu (200 nM) and inhibited by Gö6983 (10 μM) in HEK293T cells. c Statistical quantification of total EGFP intensity under all conditions. ***P = 0.0003 (C-KINACT, +PdBU/+light), *P = 0.0233 (T/A, -PdBU/+light), *P = 0.0271 (T/A, +PdBU/ + light). d Domain structures of F-KINACT and F-KINACT (Y/F). e Representative images showing F-KINACT reporting Fyn activity changes induced by human EGF (hEGF, 100 ng ml⁻¹) and inhibited by PP1 (10 μM) in HeLa cells. f Statistical quantification of total EGFP intensity under all conditions. ****P = 2.39 × 10⁻⁵ (F-KINACT, +hEGF/+light). For (c) and (f) data from 3 independent experiments. For (b) and (e) scale bars, 10 μm. For (c) and (f) statistical analysis was performed using ordinary one-way ANOVA followed by Dunnett’s multiple-comparisons test. NS not significant. Data are mean ± s.e.m. Source data are provided as a Source Data file.
3D imaging of heterogeneous PKA activity in HEK293T spheroids
a Triple-color confocal imaging of an A-KINACT dual-stable spheroid in the Z-direction. 20 layers of merged images of Hoechst-stained nuclei (blue), mCherry (red) and H2B-EGFP (green). Scale bar, 50 μm. b 3D reconstruction of individual channels showing nuclear positions (left), uniform A-KINACT expression (middle) and distribution of PKA activity within the spheroid (right). n = 3 spheroids (see Supplementary Fig. 9a for Spheroids 2 and 3).
A-KINACT for evaluating PKA responses to small molecule libraries
a Functional PKA responses to Gαs-coupled receptor agonists: Iso (100 nM, 1 µM); Iso (100 nM, 1 µM) with propranolol (Prop) (10 μM) costimulation; PGE1 (1 μM, 10 μM); PGE2 (1 μM, 10 μM); dopamine (Dopa) (1 μM, 10 μM); glucagon-like peptide 1 (GLP1) (10 nM, 30 nM). ****P < 1.00 × 10⁻²⁰ (F/I), ****P = 9.76 × 10⁻⁶ (Iso 100 nM), ****P = 3.21 × 10⁻¹¹ (Iso, 1 μM), ****P = 2.33 × 10⁻¹⁵ (PGE1, 1 μM), ****P = 2.33 × 10⁻¹⁴ (PGE1, 10 μM), ****P = 4.35 × 10⁻¹³ (PGE2, 1 μM) and ****P < 1.00 × 10⁻²⁰ (PGE2, 10 μM). b Functional PKA responses to Gαi-coupled receptor agonists: Fsk (1 μM) costimulation with LPA (500 nM) or angiotensin II (Ang-II; 10 μM). ***P = 0.0006 (LPA) and ***P = 0.0007 (Ang II). c Functional PKA responses to Gαq-coupled receptor agonists: ATP (1 μM, 10 μM); endothelin 1 (ET1) (30 nM, 100 nM) and histamine (His) (1 μM, 10 μM). **P = 0.0083 and ****P = 7.68 × 10⁻⁶. d High-throughput library screening of 160 kinase inhibitors to discover potential PKA inhibitors. Compounds with an average value below the 0.8-fold cut-off (blue dashed line) were collected. e High-throughput library screening of 137 marine natural products to discover potential PKA activators. Compounds with an average value above the 1.5-fold cut-off (red dashed line) were collected. For (a–e) data from 3 independent experiments. For (a–c) statistical analysis was performed using ordinary one-way ANOVA followed by Dunnett’s multiple-comparisons test. NS, not significant. Data are mean ± s.e.m. Source data are provided as a Source Data file.
A-KINACT effector diverts GαsR201C-PKA signaling toward the ERK pathway
a Design of A-KINACT effector for manipulating GαsR201C-mediated PKA activity and scheme for identifying transcriptional and phenotypic changes. b Top 12 enriched known TF motifs among 410 upregulated DEGs from light-induced A-KINACT control (EGFP) cells overexpressing GαsR201C. c Top 24 enriched known TF motifs among 1064 upregulated DEGs from light-induced A-KINACT effector (PKI-EGFP) cells overexpressing GαsR201C. d Venn diagrams comparing the PKA signature versus ERK signature from A-KINACT effector cells (upper) and A-KINACT control cells (lower). e GO enrichment analysis of 731 ERK-upregulated DEGs for biological processes. f Growth curves of light-reduced A-KINACT effector cells (dashed line) and A-KINACT control cells (solid line) with GαsR201C overexpression. **P = 0.005 (day 3), ***P = 0.00099 (day 4), ****P = 0.00007 (day 5), **P = 0.0042 (day 6) and **P = 0.0043 (day 7). Statistical analyses were performed using unpaired two-tailed Student’s t-tests. NS, not significant. Data from 3 independent experiments. Data are mean ± s.e.m. Source data are provided as a Source Data file.
Light-gated integrator for highlighting kinase activity in living cells

September 2024

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61 Reads

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1 Citation

Protein kinases are key signaling nodes that regulate fundamental biological and disease processes. Illuminating kinase signaling from multiple angles can provide deeper insights into disease mechanisms and improve therapeutic targeting. While fluorescent biosensors are powerful tools for visualizing live-cell kinase activity dynamics in real time, new molecular tools are needed that enable recording of transient signaling activities for post hoc analysis and targeted manipulation. Here, we develop a light-gated kinase activity coupled transcriptional integrator (KINACT) that converts dynamic kinase signals into “permanent” fluorescent marks. KINACT enables robust monitoring of kinase activity across scales, accurately recording subcellular PKA activity, highlighting PKA activity distribution in 3D cultures, and identifying PKA activators and inhibitors in high-throughput screens. We further leverage the ability of KINACT to drive signaling effector expression to allow feedback manipulation of the balance of GαsR201C-induced PKA and ERK activation and dissect the mechanisms of oncogenic G protein signaling.


Sensitive Fluorescent Biosensor Reveals Differential Subcellular Regulation of PKC

March 2024

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35 Reads

The protein kinase C (PKC) family of serine/threonine kinases, which consist of three distinctly regulated subfamilies, have long been established as critical for a variety of cellular functions. However, how PKC enzymes are regulated at different subcellular locations, particularly at emerging signaling hubs such as the ER, lysosome, and Par signaling complexes, is unclear. Here, we present a sensitive Excitation Ratiometric (ExRai) C Kinase Activity Reporter (ExRai-CKAR2) that enables the detection of minute changes in subcellular PKC activity. Using ExRai-CKAR2 in conjunction with an enhanced diacylglycerol (DAG) biosensor capable of detecting intracellular DAG dynamics, we uncover the differential regulation of PKC isoforms at distinct subcellular locations. We find that G-protein coupled receptor (GPCR) stimulation triggers sustained PKC activity at the ER and lysosomes, primarily mediated by Ca ²⁺ sensitive conventional PKC (cPKC) and novel PKC (nPKC), respectively, with nPKC showing high basal activity due to elevated basal DAG levels on lysosome membranes. The high sensitivity of ExRai-CKAR2, targeted to either the cytosol or Par-complexes, further enabled us to detect previously inaccessible endogenous atypical PKC (aPKC) activity in 3D organoids. Taken together, ExRai-CKAR2 is a powerful tool for interrogating PKC regulation in response to physiological stimuli.


Fig. 2 | General applicability of KINACT to different type of kinases. a, Domain structures of C-KINACT (WT and T/A mutant). b, Representative images showing C-KINACT and mutant C-KINACT (T/A) reporting PKC activity changes induced by PdBU (200 nM) in HEK293T cells. c, Statistical quantification of total EGFP intensity under all conditions. P=0.0008 (WT, +PdBU/+light), P=0.0004 (T/A, -PdBU/+light), P=0.0005 (T/A, +PdBU/+light). d, Domain structures of F-KINACT (WT and Y/F mutant). e, Representative images showing F-KINACT reporting Fyn activity changes induced by human EGF (hEGF, 100 nM) in HeLa cells. f, Statistical quantification of total EGFP intensity under all conditions. P=0.0003 (WT, +hEGF/+light). n = 3 independent experiments. For b and e, scale bars, 10 μ m. For c and f, statistical analysis was performed using ordinary one-way ANOVA followed by Dunnett's multiplecomparison test. NS, not significant. Data are mean ± s.e.m.
Fig. 3 | 3D imaging of heterogeneous PKA activity in HEK293T organoids. a, Triple-color confocal imaging of an A-KINACT dual-stable organoid in the Z-direction. 20-layer merged images of Hoechststained nuclei (blue), mCherry (red) and H2B-EGFP (green). Scale bar, 50 μ m. b, 3D reconstruction of individual channels showing nuclear positions (left), uniform A-KINACT expression (middle) and heterogeneity of PKA activity within the organoid. n = 3 organoids (see Supplementary Fig 7a for Organoid 2 and 3).
Light-gated Integrator for Highlighting Kinase Activity in Living Cells

March 2024

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53 Reads

Protein kinases are key signaling nodes that regulate fundamental biological and disease processes. Illuminating kinase signaling from multiple angles can provide deeper insights into disease mechanisms and improve therapeutic targeting. While fluorescent biosensors are powerful tools for visualizing live-cell kinase activity dynamics in real time, new molecular tools are needed that enable recording of transient signaling activities for post hoc analysis and targeted manipulation. Here, we develop a light-gated kinase activity coupled transcriptional integrator (KINACT) that converts dynamic kinase signals into "permanent" fluorescent marks. KINACT enables robust monitoring of kinase activity across scales, accurately recording subcellular PKA activity, highlighting PKA signaling heterogeneity in 3D cultures, and identifying PKA activators and inhibitors in high-throughput screens. We further leverage the ability of KINACT to drive signaling effector expression to allow feedback manipulation of the balance of Gα s R201C -induced PKA and ERK activation and dissect the mechanisms of oncogenic G protein signaling.



Next-Generation Genetically Encoded Fluorescent Biosensors Illuminate Cell Signaling and Metabolism

February 2024

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75 Reads

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9 Citations

Annual Review of Biophysics

Genetically encoded fluorescent biosensors have revolutionized the study of cell signaling and metabolism, as they allow for live-cell measurements with high spatiotemporal resolution. This success has spurred the development of tailor-made biosensors that enable the study of dynamic phenomena on different timescales and length scales. In this review, we discuss different approaches to enhancing and developing new biosensors. We summarize the technologies used to gain structural insights into biosensor design and comment on useful screening technologies. Furthermore, we give an overview of different applications where biosensors have led to key advances over recent years. Finally, we give our perspective on where future work is bound to make a large impact. Expected final online publication date for the Annual Review of Biophysics, Volume 53 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.


Fig. 1: Design and characterization of HaloAKARs. a, Schematic of chemigenetic HaloAKAR biosensor based on cpHaloTag labeled with JF635-CA and a phosphorylation-dependent switch that influences JF635 fluorescence intensity. b, Open-close equilibrium of JF635-CA, R = chloroalkane (CA). c, Domain structure of the three selected HaloAKARs, including linker sequences. PKA sub: PKA substrate. d, Average time courses of HeLa cells expressing HaloAKAR2.0, HaloAKAR2.1, or HaloAKAR2.2 labeled with JF635-CA and stimulated with 50 μM Fsk/100 μM IBMX and 20 μM H89 (n = 7, 10, and 13 cells for 2.0, 2.1, and 2.2, respectively). e, Maximum Fsk/IBMX-stimulated response (ΔF/F0) for HaloAKAR-JF635 biosensors. f, Representative pseudo-color widefield images of HeLa cells expressing HaloAKARs labeled with JF635-CA before and after Fsk/IBMX stimulation. Scale bars, 20 µm. g-h, Basal (g) and activated brightness (h) of HaloAKAR-JF635 biosensors. In h, the brightness of HaloTag7-JF635 is given for comparison. i, Domain structure of β-actin-targeted HaloAKAR2.2. j, Representative image of HeLa cells expressing HaloAKAR2.2-β-actin after Fsk/IBMX stimulation. Scale bar, 20 µm. k, Average time course of HeLa cells expressing HaloAKAR2.2-β-actin labeled with JF635-CA upon Fsk/IBMX stimulation (n = 4 cells). l, Domain structure of
Figure 2: Application of HaloAKARs in multiplexed biosensor imaging, 4D activity imaging and superresolution microscopy. a Three-color multiplexing of cAMP (G-Flamp1, green), Ca 2+ (RCaMP, blue) and PKA activity (HaloAKAR2.2, black) in single MIN6 cells stimulated with 20 mM tetraethylammonium chloride (TEA). b, Five-color multiplexing of Ca 2+ (B-GECO1, blue), PKC activity (sapphireCKAR (SCKAR), cyan), cGMP
Far-red chemigenetic biosensors for multi-dimensional and super-resolved kinase activity imaging

February 2024

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235 Reads

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3 Citations

Fluorescent biosensors revolutionized biomedical science by enabling the direct measurement of signaling activities in living cells, yet the current technology is limited in resolution and dimensionality. Here, we introduce highly sensitive chemigenetic kinase activity biosensors that combine the genetically encodable self-labeling protein tag HaloTag7 with bright far-red-emitting synthetic fluorophores. This technology enables five-color biosensor multiplexing, 4D activity imaging, and functional super-resolution imaging via stimulated emission depletion (STED) microscopy.




Citations (52)


... In general, precisely which metric(s) are chosen for optimisation depends on the application in mind, be it utilising MFE or ODMR, or favouring magnitude or speed of response as was of interest for the lock-in and multiplexing applications we demonstrated. As such -much like fluorescent proteins -we expect MFPs will also be engineered to make general improvements, such as to solubility, photo-stability and quantum-yield [52]. Finally, we hope that the development of MFPs can serve as the starting point for magnetically controlled biological actuators, whereby application of a local magnetic field can have downstream cellular effectssuch a technology would be of significant biomedical and biotechnological interest. ...

Reference:

Quantum Correlations in Engineered Magneto-Sensitive Fluorescent Proteins Enables Multi-Modal Sensing in Living Cells
Molecular Spies in Action: Genetically Encoded Fluorescent Biosensors Light up Cellular Signals

Chemical Reviews

... This novel approach uses circularly permuted FP as the reporting unit, resulting in phosphorylation-dependent ratiometric changes of emission upon excitation at two different wavelengths. ExRai sensors have demonstrated unprecedented sensitivity compared to FRET-based activity sensors for kinases such as PKA, PKC, AMPK, or Akt [16][17][18][19][20] . ...

Sensitive fluorescent biosensor reveals differential subcellular regulation of PKC

Nature Chemical Biology

... The idea of PKA sequestration and aggregation in mammalian cells has recently been approached by several investigators. [67][68][69] Our proposal is in line with a recently described non-canonical PKA activation mechanism via aggregation of the R1a subunit in inherited Carney complex mutations, which rendered it incapable of inhibiting the C subunit. 67 Studies from the Zhang laboratory have provided evidence that both the mammalian Ca and R1a subunits are co-recruited into R1a bodies that act as a means of compartmentalizing cAMP. ...

Molecular determinants and signaling effects of PKA RIα phase separation
  • Citing Article
  • March 2024

Molecular Cell

... This property has been recently exploited for the design of biosensors, based on genetically encoded sensing motifs fused to HaloTag in which an analyte-dependent conformational change alters the equilibrium and hence the fluorescence of the dye. [36][37][38][39] Here, we repurpose this approach to engineer a photoswitchable fluorescent system by integrating a light-responsive protein domain into the HaloTag protein ( Figure 1). Upon illumination, the conformational change of the protein photoswitch alters the dye environment, shifting its equilibrium toward the open, fluorescent state. ...

Far-red chemigenetic biosensors for multi-dimensional and super-resolved kinase activity imaging

... A diverse array of analytical techniques have been employed to quantify glucose concentrations, encompassing fluorescence [5], electrochemical methods [6], and chemiluminescence [7]. Among these techniques, electrochemical sensors stand out prominently owing to their exceptional sensitivity, remarkable selectivity, and low detection limit. ...

Next-Generation Genetically Encoded Fluorescent Biosensors Illuminate Cell Signaling and Metabolism
  • Citing Article
  • February 2024

Annual Review of Biophysics

... Fluorescent biosensors have been remarkably improved in recent years, making them indispensable tools for studying intracellular signal transduction due to their ability to monitor signaling dynamics in real time within living cells [12][13][14]. Fluorescent biosensor imaging techniques such as fluorescence resonance energy transfer (FRET), bimolecular fluorescence complementation (BiFC), and translocation-based biosensors have been developed to analyze cellular signaling and behavior in live cells [15][16][17]. Although obtaining robust FRET and BiFC signals, these techniques require a tedious optimization procedure to determine the relative locations of fluorophores and binding pairs as well as appropriate linker domains. ...

Genetically Encodable Biosensors for Ras Activity

... As alternatives to a classical second messenger role of nucleosides, physiological activation of the PKA holoenzyme in vivo may be co-activated by nucleoside binding together with a second trigger like a posttranslational modification, redox state, specific protein-protein interaction or kinase regulation by liquid-liquid phase separation (Lopez-Palacios and Andersen 2023 ; Hardy et al. 2023 ). These triggers may allosterically shift the affinity or may be required for the final activating conformational change upon binding (Khamina et al. 2022 ), giving nucleosides a more auxiliary role in the allosteric kinase regulation. ...

Molecular Determinants and Signaling Effects of PKA RIα Phase Separation
  • Citing Preprint
  • December 2023

... Fluorescence imaging technology has enabled a new approach to studying intracellular signal transduction, allowing for the analysis of biomolecule behavior in live cells under physiological conditions. Fluorescent biosensors have been remarkably improved in recent years, making them indispensable tools for studying intracellular signal transduction due to their ability to monitor signaling dynamics in real time within living cells [12][13][14]. Fluorescent biosensor imaging techniques such as fluorescence resonance energy transfer (FRET), bimolecular fluorescence complementation (BiFC), and translocation-based biosensors have been developed to analyze cellular signaling and behavior in live cells [15][16][17]. Although obtaining robust FRET and BiFC signals, these techniques require a tedious optimization procedure to determine the relative locations of fluorophores and binding pairs as well as appropriate linker domains. ...

Fluorescent biosensors illuminate the spatial regulation of cell signaling across scales

Biochemical Journal

... The most interesting thing will be to see how these tools will illuminate the field and address unresolved questions, notably how Ca 2+ is regulated within the brain and how Ca 2+ is exchanged between the cellular and interstitial compartments in physiological and pathological conditions. The review by Posner et al. (2023) highlights the potential of combining mathematical modelling with measurements of multiple signalling molecules to map out the regulatory elements within local and global signalling circuits. Different mechanisms are used by cells to spatially and temporally confine diffusible signalling molecules, including membrane or lipid partitioning, molecular assemblies anchored by scaffolding proteins and competition between opposing regulatory enzymes. ...

Fluorescent biosensor imaging meets deterministic mathematical modelling: quantitative investigation of signalling compartmentalization

... Pseudo-substrate stretches bind to the catalytic cleft of the kinase and hinder the phosphorylation of the substrate. This binding to the catalytic site is altered in response to input signals and manifested in kinase conformational changes, which in turn coordinate protein kinase activation cycles (Schmitt et al., 2022, Kemp et al., 1994. Besides intramolecular inhibition both activating and inactivating regulatory protein interactions exist and have been described for prototypical kinases such as PKA and CDKs , Taylor et al., 2005. ...

Study of spatiotemporal regulation of kinase signaling using genetically encodable molecular tools
  • Citing Article
  • December 2022

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