Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Cell-cell interactions influence all aspects of development, homeostasis, and disease. In cancer, interactions between cancer cells and stromal cells play a major role in nearly every step of carcinogenesis. Thus, the ability to record cell-cell interactions would facilitate mechanistic delineation of the role of the cancer microenvironment. Here, we describe GFP-based Touching Nexus (G-baToN) which relies upon nanobody-directed fluorescent protein transfer to enable sensitive and specific labeling of cells after cell-cell interactions. G-baToN is a generalizable system that enables physical contact-based labeling between various human and mouse cell types, including endothelial cell-pericyte, neuron-astrocyte, and diverse cancer-stromal cell pairs. A suite of orthogonal baToN tools enables reciprocal cell-cell labeling, interaction-dependent cargo transfer, and the identification of higher order cell-cell interactions across a wide range of cell types. The ability to track physically interacting cells with these simple and sensitive systems will greatly accelerate our understanding of the outputs of cell-cell interactions in cancer as well as across many biological processes. eLife digest It takes the coordinated effort of more than 40 trillion cells to build and maintain a human body. This intricate process relies on cells being able to communicate across long distances, but also with their immediate neighbors. Interactions between cells in close contact are key in both health and disease, yet tracing these connections efficiently and accurately remains challenging. The surface of a cell is studded with proteins that interact with the environment, including with the proteins on neighboring cells. Using genetic engineering, it is possible to construct surface proteins that carry a fluorescent tag called green fluorescent protein (or GFP), which could help to track physical interactions between cells. Here, Tang et al. test this idea by developing a new technology named GFP-based Touching Nexus, or G-baToN for short. Sender cells carry a GFP protein tethered to their surface, while receiver cells present a synthetic element that recognizes that GFP. When the cells touch, the sender passes its GFP to the receiver, and these labelled receiver cells become ‘green’. Using this system, Tang et al. recorded physical contacts between a variety of human and mouse cells. Interactions involving more than two cells could also be detected by using different colors of fluorescent tags. Furthermore, Tang et al. showed that, alongside GFP, G-baToN could pass molecular cargo such as proteins, DNA, and other chemicals to receiver cells. This new system could help to study interactions among many different cell types. Changes in cell-to-cell contacts are a feature of diverse human diseases, including cancer. Tracking these interactions therefore could unravel new information about how cancer cells interact with their environment. Introduction Cell-cell interactions contribute to almost all physiological and pathological states (Deb, 2014; Komohara and Takeya, 2017; Konry et al., 2016; Zhang and Liu, 2019). Despite the explosion of interest in uncovering and understanding cellular heterogeneity in tissues and across disease states, the extent to which cell-cell interactions influence cell state, drive heterogeneity, and enable proper tissue function remains poorly understood (Konry et al., 2016; Tsioris et al., 2014; Zhang and Liu, 2019). Detailed analysis of the impact of defined cell-cell interactions has illuminated critical aspects of biology; however, these analyses have been limited to a small number of juxtacrine signaling axes that are tractable to study (Dustin and Choudhuri, 2016; Meurette and Mehlen, 2018; Yaron and Sprinzak, 2012). Interactions between cancer cells and stromal cells play central roles in cancer initiation, progression, and metastasis (Kitadai, 2010; Orimo and Weinberg, 2006). While secreted factors relaying pro- or anti-tumorigenic signals have been extensively investigated, the impact of direct physical interactions between cancer cells and stromal cells remains understudied (Bendas and Borsig, 2012; Dittmer and Leyh, 2014; Nagarsheth et al., 2017). A greater understanding of the constellation of direct interactions that cancer cells undergo will not only deepen our understanding of tumor ecology but also has the potential to uncover novel therapeutic opportunities (Nagarsheth et al., 2017; Swartz et al., 2012). Furthermore, how diverse cell-cell interactions differentially impact cancer cells at different stages of carcinogenesis and within different organ environments remains largely uncharacterized. Molecular methods to profile cell state, including in situ approaches within intact tissues, largely fail to uncover the causal relationship between cell-cell interactions and the underlying biology (Giladi et al., 2020; Halpern et al., 2018). Computational and experimental methods to characterize cell-cell interactions yield additional layers of dimensionality; however, modalities to capture cell-cell interactions are limited (Boisset et al., 2018; Morsut et al., 2016; Pasqual et al., 2018). Much as diverse systems to detect and quantify protein-protein interactions have revolutionized our biochemical understanding of molecular systems, the development of novel systems to detect and quantify cell-cell interactions will accelerate the mapping of the interaction networks of multicellular systems. Endogenous cell-cell interactions can result in transfer of surface proteins between cells, mainly through either trans-endocytosis or trogocytosis (Langridge and Struhl, 2017; Li et al., 2019; Ovcinnikovs et al., 2019). Thus, we sought to integrate this phenomenon with fluorescent protein tagging to label cells that have undergone direct interactions. We describe a surprisingly robust system (which we term GFP-based Touching Nexus or G-baToN) that enables sensitive and specific interaction-dependent labeling of cancer cells and various primary stromal cells, including endothelial cells, T cells and neurons. We extensively characterize this approach and describe several novel applications of this versatile system. Results G-baToN enables cell-cell interaction-dependent labeling To create a system in which a fluorescent signal could be transferred between neighboring cells, we adapted a synthetic ligand-receptor system based on the expression of surface GFP (sGFP) on sender cells and a cell surface anti-GFP (αGFP) nanobody on receiver cells (Fridy et al., 2014; Lim et al., 2013; Morsut et al., 2016). Co-culturing sGFP sender cells with αGFP receiver cells led to GFP transfer and labeling of the receiver cells (Figure 1A,B and Figure 1—figure supplement 1A). Receiver cell labeling required direct cell-cell contact, active membrane dynamics, and pairing between sGFP and its cognate αGFP receptor (Figure 1C,D and Figure 1—figure supplement 1B, C). Notably, sGFP transfer was accompanied by reduced GFP on the sender cells, downregulation of αGFP from the surface of the receiver cells and was partially blocked by chemical inhibitors of endocytosis – all consistent with active GFP transfer and internalization into receiver cells (Figure 1—figure supplement 1D–F). Figure 1 with 2 supplements see all Download asset Open asset GFP-based Touching Nexus (G-baToN) leads to cell-cell interaction-dependent receiver cell labeling. (a) Schematic of the G-baToN system. Surface GFP (sGFP) on a sender cell is transferred to a receiver cell expressing a cell surface anti-GFP nanobody (αGFP) leading to GFP labeling of the ‘touched’ receiver cell. (b) GFP transfer from sGFP-expressing KPT lung cancer sender cells (marked by intracellular tdTomato) to αGFP-expressing 293 receiver cells. Receiver cell labeling is sGFP- and αGFP- dependent. Control sender cells do not express sGFP. Control receiver cells do not express αGFP. Cytoplasmic GFP (Cyto-GFP) is not transferred to receiver cells. Sender and receiver cells were seeded at a 1:1 ratio and co-cultured for 24 hr. Receiver cells were defined as TomatonegPIneg cells. (c) GFP transfer to 293 receiver cells requires direct cell-cell contact. Receiver cells separated from sender cells by a transwell chamber are not labeled. Sender and receiver cells were seeded in upper and lower chambers respectively at a 1:1 ratio and cultured for 24 hr. Receiver cells were defined as TomatonegPIneg cells. (d) GFP transfer to 293 receiver cells requires sGFP-αGFP interaction and is blocked by an anti-GFP antibody in a dose-dependent manner. sGFP sender cells were pre-incubated with the indicated concentration of anti-GFP antibody for 2 hr, washed with PBS, and then co-cultured with receiver cells at a 1:1 ratio for 24 hr. Receiver cells were defined as TomatonegPIneg cells. (e) Time-lapse imaging of GFP transfer from a sGFP-expressing sender cell to an αGFP-expressing receiver cell. Time after contact is indicated. Receiver cell is outlined with white dashed line. Scale bar: 10 μm. (f) Analysis of GFP Mean Fluorescence Intensity (MFI) of αGFP receiver cells (marked by intracellular BFP) co-cultured with sGFP sender cells (marked by intracellular tdTomato) co-cultured for the indicated amount of time. Sender and receiver cells were seeded at a 1:1 ratio. Receiver cells were defined as TomatonegPInegBFPpos cells. (g) Percentage of labeled αGFP receiver cells after co-culture with different numbers of sender cells for 24 hr. Receiver cells were defined as TomatonegPInegBFPpos cells. (h) Detection of rare labeled αGFP receiver cells after co-culture with sGFP sender cells at approximately a 1:105 ratio for 24 hr. Receiver cells were defined as TomatonegPInegBFPpos cells. To characterize the kinetics of G-baToN-mediated receiver cell labeling, we performed co-culture time course experiments with time-lapse imaging and flow cytometry readouts. Time-lapse imaging showed rapid transfer and internalization of GFP by receiver cells (Figure 1E and Video 1). GFP transfer could be detected within five minutes of co-culture and was half-maximal after 6 hr (Figure 1F and Figure 1—figure supplement 1G-H). Importantly, GFP fluorescence in receiver cells decayed rapidly after isolation of touched receiver cells from sender cells, thus documenting the transient labeling of receiver cells (Figure 1—figure supplement 1I). To determine the sensitivity of this system, we co-cultured receiver cells with different ratios of sender cells. The fraction of labeled receiver cells was proportional to the number of sender cells, and even the addition of very few sender cells (representing less than one sender cell to 105 receiver cells) was sufficient to label rare receiver cells (Figure 1G,H). Thus, the transfer of GFP to αGFP-expressing cells is a rapid and sensitive method to mark cells that have physically interacted with a predefined sender population. Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg Time-lapse movie of a sGFP sender cell transferring GFP into a αGFP receiver cell. Fluorescence transfer efficiency is modulated by transmembrane domains and nanobody affinity To further characterize the interaction reporter system, we deconstructed the G-baToN design into three functional modules: (1) the transmembrane domain of αGFP on the receiver cells; (2) the pairing between GFP and αGFP; and (3) the transmembrane domain of sGFP on the sender cells. We initially used a published sGFP-αGFP pair in which the Notch1 transmembrane domain links the LaG17-αGFP nanobody onto the receiver cell surface and the PDGFR transmembrane domain links sGFP onto the sender cell surface (Morsut et al., 2016). Replacement of the Notch1 transmembrane domain of αGFP with different transmembrane domains allowed us to quantify their impact on GFP transfer efficiency. The VEGFR2 transmembrane domain enabled the highest transfer efficiency, resulting in about a threefold increase relative to the original design (Figure 2A–C). We next replaced the LaG17-αGFP nanobody with αGFP nanobodies with varying affinity for GFP (Figure 2D,E). While nanobodies exhibiting the highest affinities performed similarly, we noted a minimal affinity required for GFP transfer (Figure 2F). Overall, the efficiency of GFP transfer correlated with GFP affinity. Lastly, permutation of the transmembrane domain of sGFP on the sender cell revealed that the rate of retrograde transfer of αGFP-VEGFR2-BFP from receiver to sender cells was influenced by the sGFP transmembrane domain (Figure 2G–I). The PDGFR transmembrane domain minimized bidirectional transfer and thus was the optimal design for minimizing retrograde transfer which could generate false-positive signals (Figure 2G–I). Collectively, the permutation of the transmembrane domains anchoring sGFP and αGFP, as well as varying the αGFP nanobody affinity identified designs that maximized unidirectional receiver cell labeling. Figure 2 Download asset Open asset Transmembrane domains and the nanobody affinity impact sGFP transfer and receiver cell labeling. (a) Schematic of the sender and receiver cells used to determine the impact of different αGFP transmembrane (TM) domains. TM domains contain the TM domain itself as well as membrane proximal regions from the indicated mouse (m) and human (h) proteins. (b) Different TM domains impact cell surface αGFP expression on 293 receiver cells. Membrane αGFP was quantified by anti-Myc staining. Control receiver cells do not express any nanobody. Mean +/- SD of Myc MFI of triplicate cultures is shown. (c) VEGFR2 TM domain on αGFP receiver cells enable highest GFP transfer efficiency. Receiver cells expressing αGFP linked to different TM domains were co-cultured with sGFP sender cells at a 1:1 ratio for 6 hr. Receiver cells were defined as TomatonegPIneg cells. (d) Schematic of the sender and receiver cells used to determine the impact of different αGFP nanobodies on G-baToN-based labeling. (e) Different nanobodies exhibit different levels of expression on 293 receiver cells. Total αGFP expression was assessed by BFP intensity. Mean +/- SD of GFP MFI of triplicate cultures is shown. (f) αGFP affinity influences transfer of GFP to touched 293 receiver cells. Receiver cells expressing different αGFP nanobodies were co-cultured with sGFP sender cells at a 1:1 ratio for 6 hr. GFP transfer was assessed by flow cytometry. GFP intensity on TomatonegPInegBFPpos receiver cells is shown as mean +/- SD of triplicate cultures. (g) Schematic of the sender and receiver cells used to determine the impact of different sGFP TM domains on G-baToN-based labeling. TM domains contain the TM domain itself as well as membrane proximal regions from the indicated mouse (m) proteins. (h) Different TM domains on sGFP impact its expression in 293 sender cells. sGFP expression in sender cells was assessed by flow cytometry for GFP. Mean +/- SD of GFP MFI of triplicate cultures is shown. (i) PDGFR TM domain on sGFP minimized retrograde transfer of αGFP from receiver cells to 293 sGFP sender cells. αGFP transfer to sGFP sender cells was determined as the percentage of mCherryposGFPpos sender cells that were also BFPpos. Cells were co-cultured for 6 hr at a 1:1 ratio. Mean +/- SD of triplicate cultures is shown. Tracking cancer-stroma interactions using G-baToN Cancer cells interact with a variety of stromal cells at both the primary and metastatic sites (Kota et al., 2017; Nielsen et al., 2016). Thus, we employed the G-baToN system to record various cancer-stroma interactions in conventional 2D and 3D microfluidic culture systems as well as in vivo. Co-culturing sGFP-expressing lung adenocarcinoma cells with primary human umbilical vein endothelial cells (HUVECs) in a 2D format led to robust endothelial cell labeling (Figure 3A,B). Additionally, within 3D microfluidic chips, pre-seeded HUVECs expressing αGFP were robustly labeled following co-incubation with sGFP-expressing lung adenocarcinoma cells (Figure 3E–G). Thus, the G-baToN system is able to efficiently record cancer cell-endothelial cell interactions across multiple culture conditions. Figure 3 Download asset Open asset G-baToN can be detect cancer cell-endothelial cell and endothelial cell-smooth muscle cell interactions. (a, b) G-baToN can detect cancer cell-endothelial cell (EC) interactions. HUVECs expressing αGFP were co-cultured with or without Tomatopos sGFP-expressing lung cancer sender cells at a 1:1 ratio for 24 hr. (a) Representative images of Tomatopos sGFP-expressing lung cancer sender cells co-cultured with either control HUVEC receiver cells (HUVECs expressing BFP) or αGFP HUVEC receiver cells at a 1:1 ratio for 24 hr. Scale bars = 50 μm. (b) MFI of GFP on PInegTomatonegBFPposCD31pos Receiver cells was assessed by flow cytometry and is shown as mean +/- SD of triplicate cultures. **p<0.01, n = 3. (c,d) G-baToN can detect endothelial cell (EC)-smooth muscle cell (SMC) interactions. Primary human umbilical artery smooth muscle cells (HUASMC) expressing αGFP were co-cultured with or without sGFP-expressing HUVEC sender cells at a 1:1 ratio for 24 hr. (c) Representative images of sGFP-expressing HUVEC sender cells co-cultured with either control HUASMC receiver cells (expressing BFP) or αGFP HUASMC receiver cells at a 1:1 ratio for 24 hr. Scale bars = 50 μm. (d) MFI of GFP on PInegBFPpos receiver cells was assessed by flow cytometry and is shown as mean +/- SD of triplicate cultures. **p<0.01, n = 3. (e,f,g) G-baToN can detect cancer cell-endothelial cell interactions in 3D-microfluidic culture. (e) Details on design of 3D-microfluidic devices for cancer cell-endothelial cell co-culture. (f) Representative images of Tomatopos sGFP-expressing lung cancer sender cells co-cultured with either control HUVEC receiver cells (HUVECs expressing BFP) or αGFP HUVEC receiver cells at a 1:10 ratio for 24 hr. Scale bars = 200 μm. (g) Average number of GFPpos HUVEC after co-culture with cancer cells for 24 hr. 10 areas from three chips with 200X magnification were used for the quantification. **p<0.01, n = 10. Given the importance of interactions with adaptive immune cells during carcinogenesis (Crespo et al., 2013; Joyce and Fearon, 2015), we assessed the ability of the G-baToN system to track the interaction of primary human CD4 and CD8 T cells with lung cancer cells. αGFP-expressing CD4 and CD8 T cells that interacted with sGFP-expressing lung cancer cells in culture were specifically labeled (Figure 4A–C). To test the ability of the G-baToN system to capture cancer cell-T cell interactions in vivo, we established lung tumors from a sGFP-expressing lung adenocarcinoma cell line prior to intravenous transplantation of αGFP-expressing CD4 T cells. 24 hr after T cell transplantation, over 60% of αGFP-expressing CD4 T cells within the tumor-bearing lungs were labeled with GFP, while control CD4 T cells remained unlabeled (Figure 4D,E). Thus, the G-baToN system is capable of recording cancer cell-T cell interactions both in vitro and in vivo. Figure 4 Download asset Open asset G-baToN can detect cancer cells – T cells interactions. (a,b,c) G-baToN can detect cancer cell-T cell interactions in vitro. (a) Primary human CD4pos T cells were co-cultured with sGFP-expressing lung cancer sender cells (A549 cells) at a 2:1 ratio for 24 hr. Representative image of A549 cell and CD4 T cell interactions. Scale bars = 10 μm. BF = bright field (b,c) A549 cells expressing sGFP can transfer GFP to αGFP primary human CD4pos (b) or CD8pos (c) T cells after co-culture at a 1:1 ratio for 24 hr. Receiver cells were defined as Near-IRnegBFPposCD4pos or CD8pos T cells. (d,e) G-baToN can detect cancer cell-T cell interactions in vivo. (d) Experiment design for cancer cell-T cell interactions in vivo. 1 × 106 sGFP-expressing lung cancer sender cells were transplanted into NSG mice at day 0. 4 × 106 αGFP primary human CD4pos T cell were transplanted into tumor-bearing mice at day 21. One day after T cell transplantation (day 22), T cells in the mouse lung were analyzed by FACS. (e) sGFP-expressing cancer cell can transfer GFP to αGFP-expressing primary human CD4pos T cells. Receive cells were defined as PInegBFPposCD4posT cells. Recent studies have demonstrated a supportive role for neurons within the primary and metastatic niche in the context of brain (Venkatesh et al., 2019; Zeng et al., 2019). To determine whether G-baToN can record cancer cell-neuron interactions, we co-cultured sGFP-expressing lung adenocarcinoma cells with primary cortical neurons expressing αGFP. Physical contact between cancer cells and neuronal axons led to punctate-like GFP granule transport into receiver neurons (Figure 5A–B). These results demonstrate the successful application of G-baToN system to record a variety of cancer cell-stromal cell interactions. Figure 5 with 1 supplement see all Download asset Open asset G-baToN can detect cancer cell–neuron and astrocyte-neuron interactions. (a) Representative image of sGFP-expressing cancer sender cells co-cultured with either control neuron receiver cells or αGFP neuron receiver cells at a 1:1 ratio for 24 hr. Neurons were stained with Microtubule Associated Protein 2 (Map2). Scale bars = 50 μm. (b) Quantification of a using images from 10 different fields. Each dot represents a field. The bar indicates the mean +/- SD. GFPpos neurons were defined as Map2posTomatoneg cells with GFP. **p<0.01, n = 10. (c) Representative images of sGFP-expressing astrocyte sender cells co-cultured with either control neuron receiver cells or αGFP neuron receiver cells at a 1:2 ratio for 24 hr. Neurons were stained with Map2. Scale bars = 50 μm. Higher magnification of the boxed areas are shown on the right. (d) Quantification of c using images from 10 different fields. Each dot represents a field. The bar indicates the mean +/- SD. GFPpos neurons were defined as Map2pos cells with GFP. **p<0.01, n = 10. G-baToN can be applied in a wide range of cell types To assess the generalizability of the G-baToN system across cell types, we expressed αGFP in a panel of cell lines and primary cells. Each receiver cell type was able to uptake GFP from sGFP-expressing lung cancer sender cells upon cell-cell contact (Figure 5—figure supplement 1A). Furthermore, diverse cancer cell lines and primary cell types expressing sGFP were able to transfer GFP to αGFP-expressing HEK293 receiver cells (Figure 5—figure supplement 1B–F). As anticipated, receiver cell labeling required sGFP-expression on the sender cell and αGFP expression on the receiver cells. Thus, G-baToN-based labeling extends beyond transformed cell types and can label diverse primary cell types in co-culture. To further test the generalizability of the system and determine whether primary cells can serve as both sender and receiver cells, we assessed GFP transfer between interacting primary cells in the context of two well-established heterotypic cell-cell interactions: endothelial cells interacting with smooth muscle cells and astrocytes interacting with neurons. Co-culturing sGFP-expressing HUVEC and αGFP-expressing primary human umbilical vein smooth muscle cells (HUVSMC) resulted in efficient receiver smooth muscle cell labeling (Figure 3C,D). Furthermore, sGFP-expressing astrocytes were able to transfer GFP to αGFP-expressing cortical neurons (Figure 5C,D). Collectively, these results document the efficiency of G-baToN-based cell labeling across diverse cell types. Multicolor labeling enables recording of reciprocal and higher-order interactions Given the high efficiency with which sGFP labels receiver cells upon interaction with cognate sender cells, we tested whether other surface antigen/antibody pairs could lead to protein transfer and labeling. Due to the cross reactivity of αGFP with BFP, co-culture of surface BFP (sBFP) sender cells with αGFP receiver cells generated BFP-labeled receiver cells (Fridy et al., 2014; Figure 6—figure supplement 1A, B). Orthogonal systems consisting of surface-mCherry/αmCherry (LaM4)(Fridy et al., 2014) and surface-GCN4-GFP/αGCN4 (single-chain variable fragment, scFV)(Tanenbaum et al., 2014) also led to efficient and specific receiver cell labeling (Figure 6—figure supplement 1C–F). Thus, the G-baToN labeling system can be extended to additional antigen/antibody pairs. We next integrated these orthogonal systems to enable reciprocal labeling and detection of higher order multi-cellular interactions. Engineering cells with these orthogonal systems in an anti-parallel fashion should enable reciprocal labeling of both interacting cells. Co-culture of cells expressing sGFP and αmCherry with cells expressing smCherry and αGFP resulted in reciprocal labeling of both interacting cell types (Figure 6A,B, and Figure 6—figure supplement 2A). This reciprocal labeling system may be particularly useful when the interaction elicits changes in both interacting cell types. Using orthogonal ligand-receptor pairs, we also created an AND gate dual labeling strategy. Specifically, co-expression of αmCherry and αGFP on receiver cells enabled dual color labeling of receiver cells that had interacted with smCherry-expressing, sGFP-expressing, or both sender cell types (Figure 6C,D, and Figure 6—figure supplement 2B). Analogously, we achieved dual-color labeling of receiver cells by leveraging the ability of αGFP to bind to both sGFP and sBFP (Figure 6E,F). Thus, derivatives of the G-baToN system allow for additional degrees of resolution of complex cell-cell interactions. Figure 6 with 2 supplements see all Download asset Open asset Multicolor-baToN systems enable recording of higher-order interactions. (a) Diagram of the reciprocal baToN system. Cell A expresses sGFP and αmCherry (tagged by intracellular BFP), Cell B expresses smCherry and αGFP (tagged by Myc-tag). (b) Representative FACS plots of cell A and cell B monocultures (left two panels) and after co-culture at a 5:1 ratio for 24 hr. Percent of labeled cells is indicated as mean +/- SD of triplicate cultures. (c) Schematic of the AND gate-baToN system. sGFP and smCherry sender cells express either sGFP or mCherry. Dual receiver cells express both αGFP (LaG17, tagged by Myc-tag) and αmCherry (LaM4, tagged by intracellular BFP). (d) Representative FACS plots of dual receiver 293 cells cultured with the indicated 293 sender cells at 1:1 (for single sender cell) or 1:1:1 (for dual sender cells) ratios. Percent of labeled receiver cells (defined as BFPpos) after 24 hr of co-culture is indicated as mean +/- SD of triplicate cultures. (e) Diagram of the BFP/GFP AND gate baToN system. sBFP sender cells express intracellular Tomato and surface BFP, sGFP sender cells express intracellular Tomato and surface GFP. Common receiver cells expressed αGFP. (f) Representative FACS plots of common receiver 293 cells cultured with the indicated Tomatopos sender cells at 1:1 (for single sender cell) or 1:1:1 (for dual sender cells) ratios. Receiver cells were defined as TomatonegPIneg. Percent of labeled common receiver cells after 24 hr of co-culture is indicated as mean +/- SD of triplicate cultures. Labeling with HaloTag-conjugated fluorophores enhances sensitivity and signal persistence We next extended our labeling system further by generating sender cells expressing the HaloTag protein fused to sGFP (sHalo-GFP; Figure 7A; Los et al., 2008). Covalent attachment of a synthetic fluorophore to sHalo-GFP enabled specific loading onto sender cells (Figure 7B). Co-culture of Alexa Fluor 660 (AF660)-loaded sHalo-GFP sender cells with αGFP receiver cells enabled co-transfer of both GFP and AF660 (Figure 7C). Compared to GFP, transfer of the chemical fluorophore using sHalo-GFP-based labeling of receiver cells led to increased signal-to-noise ratio and higher sensitivity (Figure 7C,D). Importantly, changing from a protein (GFP) to a chemical fluorophore also extended the half-life of labeling, thus enabling partially tunable persistence of labeling after touching (Figure 7E). Figure 7 Download asset Open asset The HaloTag-baToN system enables quantitative and sensitive cell-cell interaction-dependent receiver cell labeling. (a) Diagram of HaloTag-baToN system. Sender cells (marked by intracellular 2A-mCherry) express surface HaloTag-GFP fusion which can be loaded with HaloTag ligands (in this example AF660). Receiver cells express αGFP (LaG17, tagged by intracellular BFP). (b) Labeling of HaloTag-expressing sender cells with AF660 fluorophore. Representative FACS plots of KP (lung adenocarcinoma) sender cells expressing either sGFP or sGFP-sHaloTag incubated with AF660-conjugated HaloTag ligand for 5 min on ice. AF660 specifically labeled sHaloTag-GFP sender cells but not sGFP sender cells. (c) Representative plot of GFP and AF660 intensity in αGFP 293 receiver cells co-cultured with HaloTag-GFP KP sender cells at a 1:1 ratio for 6 hr. Receiver cells were defined as mCherrynegPInegBFPpos cells. (d) AF660 transfer to αGFP 293 receiver cells is rapid after cell-cell interaction. AF660 MFI shift was detected after mixing sHalo-GFP sender cells and αGFP receiver cells and co-culture for 10 min. AF660 MFI shift was more dramatic than GFP. Receiver cells were defined as mCherrynegPInegBFPpos cells. (e) Slower AF660 quenching in touched receiver cells after removing sHalo-GFP sender cells. A
Cell-cell interactions influence all aspects of development, homeostasis, and disease. In cancer, interactions between cancer cells and stromal cells play a major role in nearly every step of carcinogenesis. Thus, the ability to record cell-cell interactions would facilitate mechanistic delineation of the role of the cancer microenvironment. Here, we describe GFP-based Touching Nexus (G-baToN) which relies upon nanobody-directed fluorescent protein transfer to enable sensitive and specific labeling of cells after cell-cell interactions. G-baToN is a generalizable system that enables physical contact-based labeling between various human and mouse cell types, including endothelial cell-pericyte, neuron-astrocyte, and diverse cancer-stromal cell pairs. A suite of orthogonal baToN tools enables reciprocal cell-cell labeling, interaction-dependent cargo transfer, and the identification of higher order cell-cell interactions across a wide range of cell types. The ability to track physically interacting cells with these simple and sensitive systems will greatly accelerate our understanding of the outputs of cell-cell interactions in cancer as well as across many biological processes.
<p>Summary of GSEA analysis on Sik-targeted tumors using gene sets from previous publications centered on transcriptional profiling of the Lkb1-deficient state.</p>
Although multiple decision support systems have been built for physicians, efficient delivery of valid and complete medical knowledge remains an elusive goal. In this paper we describe a new project, the Stanford Health Information Network for Education (SHINE). SHINE unifies core medical resources in an intuitive interface to support clinical decision making. Included in the description is a novel paradigm for continuing medical education (CME).
<div>Abstract<p>Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most common subtype. Many oncogenes and tumor suppressor genes are altered in this cancer type, and the discovery of oncogene mutations has led to the development of targeted therapies that have improved clinical outcomes. However, a large fraction of lung adenocarcinomas lacks mutations in known oncogenes, and the genesis and treatment of these oncogene-negative tumors remain enigmatic. Here, we perform iterative <i>in vivo</i> functional screens using quantitative autochthonous mouse model systems to uncover the genetic and biochemical changes that enable efficient lung tumor initiation in the absence of oncogene alterations. Generation of hundreds of diverse combinations of tumor suppressor alterations demonstrates that inactivation of suppressors of the RAS and PI3K pathways drives the development of oncogene-negative lung adenocarcinoma. Human genomic data and histology identified RAS/MAPK and PI3K pathway activation as a common feature of an event in oncogene-negative human lung adenocarcinomas. These Onc-negative<sup>RAS/PI3K</sup> tumors and related cell lines are vulnerable to pharmacologic inhibition of these signaling axes. These results transform our understanding of this prevalent yet understudied subtype of lung adenocarcinoma.</p>Significance:<p>To address the large fraction of lung adenocarcinomas lacking mutations in proto-oncogenes for which targeted therapies are unavailable, this work uncovers driver pathways of oncogene-negative lung adenocarcinomas and demonstrates their therapeutic vulnerabilities.</p></div>
Most cancers are diagnosed in persons over the age of sixty, but little is known about how age impacts tumorigenesis. While aging is accompanied by mutation accumulation - widely understood to contribute to cancer risk - it is also associated with numerous other cellular and molecular changes likely to impact tumorigenesis. Moreover, cancer incidence decreases in the oldest part of the population, suggesting that very old age may reduce carcinogenesis. Here we show that aging represses tumor initiation and growth in genetically engineered mouse models of human lung cancer. Moreover, aging dampens the impact of inactivating many, but not all, tumor suppressor genes with the impact of inactivating PTEN, a negative regulator of the PI3K/AKT pathway, weakened to a disproportionate extent. Single-cell transcriptomic analysis revealed that neoplastic cells from tumors in old mice retain many age-related transcriptomic changes, showing that age has an enduring impact that persists through oncogenic transformation. Furthermore, the consequences of PTEN inactivation were strikingly age-dependent, with PTEN deficiency reducing signatures of aging in cancer cells and the tumor microenvironment. Our findings suggest that the relationship between age and lung cancer incidence may reflect an integration of the competing effects of driver mutation accumulation and tumor suppressive effects of aging.
