In vivo single cell analysis reveals Gata2 dynamics in cells transitioning to hematopoietic fate

Eich et al. reveal the dynamic expression of the Gata2 transcription factor in single aortic cells transitioning to hematopoietic fate by vital imaging of Gata2Venus mouse embryos. Pulsatile expression level changes highlight an unstable genetic state during hematopoietic cell generation.

Recent studies have identified a growing list of TFs that show pulsatile dynamic behavior (Lahav et al., 2004;Nelson et al., 2004;Cai et al., 2008;Cohen-Saidon et al., 2009;Locke et al., 2011;Levine et al., 2013;Purvis and Lahav, 2013;Ryu et al., 2016;Zambrano et al., 2016). A pulse is detected when a critical threshold of TF molecules accumulate and ends when they are degraded/deactivated. The presence of pulsatile expression for various regulators in bacteria (Locke et al., 2011;Young et al., 2013), yeast (Garmendia-Torres et cell fate is established through coordinated gene expression programs in individual cells. regulatory networks that include the Gata2 transcription factor play central roles in hematopoietic fate establishment. Although Gata2 is essential to the embryonic development and function of hematopoietic stem cells that form the adult hierarchy, little is known about the in vivo expression dynamics of Gata2 in single cells. Here, we examine Gata2 expression in single aortic cells as they establish hematopoietic fate in Gata2Venus mouse embryos. time-lapse imaging reveals rapid pulsatile level changes in Gata2 reporter expression in cells undergoing endothelial-to-hematopoietic transition. Moreover, Gata2 reporter pulsatile expression is dramatically altered in Gata2 +/− aortic cells, which undergo fewer transitions and are reduced in hematopoietic potential. our novel finding of dynamic pulsatile expression of Gata2 suggests a highly unstable genetic state in single cells concomitant with their transition to hematopoietic fate. this reinforces the notion that threshold levels of Gata2 influence fate establishment and has implications for transcription factor-related hematologic dysfunctions. Dalal et al., 2014), and the mammalian stress response and signaling pathways (Lahav et al., 2004;Nelson et al., 2004;Kageyama et al., 2008;Cohen-Saidon et al., 2009;Kholodenko et al., 2010;Tay et al., 2010;Batchelor et al., 2011;Albeck et al., 2013;Yissachar et al., 2013) suggests that it is a common process. Pulsing may provide a time-based mode of regulation, where an input typically modulates the pulse frequency, amplitude, and/or duration of individual TFs to control downstream target gene expression. This dynamic behavior and pulsatile expression of TFs in single cells is implicated in cell transitions and fate decisions (Nelson et al., 2004;Shimojo et al., 2008;Kobayashi et al., 2009;Tay et al., 2010;Pourquié, 2011;Imayoshi et al., 2013;Kueh et al., 2013Kueh et al., , 2016Neuert et al., 2013;Stern and Piatkowska, 2015) and includes, for example the NF-κb and Notch signaling pathways Levine et al., 2013;Purvis and Lahav, 2013;Isomura and Kageyama, 2014).
Although much information is emerging on transcriptomic signatures and molecules affecting the development of the hematopoietic system (Lichtinger et al., 2012;Swiers et al., 2013;Solaimani Kartalaei et al., 2015;Goode et al., 2016;Zhou et al., 2016), dynamic expression is still a largely unexplored area. We set out to examine the dynamics of Gata2 expression during the establishment of hematopoietic fate in the aortic endothelium, because Gata2 is a downstream target of the Notch pathway (Robert-Moreno et al., 2005;Gama-Norton et al., 2015) and is known to affect EHT (Kumano et al., 2003;Ling et al., 2004;de Pater et al., 2013), and the dosage of Gata2 is of major importance for normal hematopoietic development (Ling et al., 2004;Khandekar et al., 2007;Tipping et al., 2009;de Pater et al., 2013;Gao et al., 2013). Here, we demonstrate for the first time the pulsatile expression of a Gata2 reporter during the process whereby hematopoietic cells are generated from HECs. By vital imaging of single cells in the mouse embryonic aorta (WT and Gata2 heterozygous mutant), we show that cell states during EHT correlate with Gata2 reporter expression duration, levels (amplitude changes), and pulse periodicity, thus supporting the notion that Gata2 levels and dynamic behavior are linked to hematopoietic fate.
Venus expression was found in single cells of the aortic endothelium, cells bulging from the endothelial wall, and IAHCs (Fig. 1, A and B), all of which are CD31 + . ECs (CD31 + ckit − ) are flattened ckit − cells in the vascular wall, and for this study, G2V-expressing (V + ) ECs are referred to as HECs. G2V-expressing ckit + cells undergoing a change in morphology as they emerge from the wall are referred to as bulging cells (BCs; V + CD31 + ckit + ). IAHCs (V + CD31 + ckit + ) are the rounded cells found in clusters adjacent to the endothelial layer ( Fig. 1 C). Flow cytometric analysis (FACS) showed that varying levels (medium and high) of Venus expression could be detected in the CD31 + ckit + cells (Fig. 1 D). Upon sorting CD31 + ckit + Venus med and Venus high cells, RNA-sequencing analysis ( Fig. 1 E) showed medium and high levels of Gata2 transcripts, respectively. Furthermore, Venus med and Venus high expression levels correctly reflect Gata2 protein levels, as confirmed by Western blotting of sorted cell fractions from adult G2V bone marrow (Fig. S1 A). Equivalent ratios of quantified Gata2 to Venus protein signal were found for all the sorted cell populations. Hence, the G2V reporter allows the accurate tracking of Gata2 expression in single live cells during EHT.
Vital imaging of G2V embryo transversal sections through the AGM was performed at 15-min intervals for 10-15 h (Videos 1, 2, and 3). Imaging data were examined for EHT events in which Venus-expressing (V + ) hematopoietic cells emerge from the aortic wall. In 15 independent time-lapse imaging experiments with a total of 49 sections, we observed 13 EHT events of V + CD31 + (ckit + ) hematopoietic cells emerging from the V + CD31 + (ckit − ) endothelium of E10 embryos (32-37 somite pairs [SPs]; Fig. 2 and Videos 1, 2, and 3). Taking into account the thickness of the embryo section and length of the aorta (forelimb to hindlimb where IAHC are found), we calculated that there are ∼20 EHT events per embryo. This is in contrast to the 1.7 EHT events per embryo previously observed in the Ly6aGFP reporter aorta imaging studies (Boisset et al., 2010). In the cases in which we imaged an EHT event, visual analysis of time-lapse images revealed changes in the mean fluorescent intensity (MFI) suggestive of flexible and pulsatile Gata2 expression in BCs (Fig. 2, A-C) and, to a lesser extent, IAHCs (Fig. 2 D and Fig. S3 F).
V + cells were counted in the first frame image of time-lapse experiments (n = 15; 1,126 cells). When the numbers were calculated per total aorta, 660 ± 87 V + cells were found in the endothelial layer (HECs), followed by 305 ±131 in BCs (7% of which undergo EHT) and 266 ±132 in IAHCs. Highly sensitive FACS of E10.5 G2V AGMs confirmed the microscopy results, showing the highest numbers of V + cells in the aortic endothelium (CD31 + ckit − ; 1,076 HECs) and fewer V + cells in the CD31 + ckit + hematopoietic population (680 BCs and IAHCs; Table 1). These numbers are higher than what has been published previously for the Ly6aGFP reporter in the E10.5 AGM (831 HECs, and 261 BCs and IAHCs at 34 SPs; Solaimani Kartalaei et al., 2015), indicating that G2V expression is encompassing more EHT cells than the Ly6aGFP reporter ( Fig. S1 B). Additionally, the majority of V + HECs, BCs, and IAHCs are found on the ventral side of the aorta, with only 23% of V + cells on the dorsal side (Fig. 3 A).

expression dynamics differ among Hecs, Bcs, and IAHcs
Time-lapse imaging allowed us to follow Venus expression up to 15 h without confounding bleaching effects. To quantitate Gata2 expression levels and dynamics during the transition of HECs to BCs and BCs to IAHCs, confocal images of single aortic cells were deconvoluted to improve the signal to noise ratio and analyzed using commercial and custom-made tools for tracking cells in four dimensions (Fig. S2). Only cells tracked for at least 10 consecutive frames were included, and tracking in three dimensions guaranteed that the observed changes in Venus intensity were not due to cells moving in or out of the imaging plane ( Fig. 3 B). Venus MFI values plotted over time ( Fig. S2; details in Materials and methods) showed expression in individual cells to be dynamic (Videos 1, 2, and 3), and as shown in Fig. 2, EHT was accompanied by increased, decreased, and/or alternating levels of Venus expression. Changes in Venus MFI were observed in both the raw and deconvoluted data, ruling out the possibility that deconvolution introduced artifacts (Fig. S3, A and B).
To ensure that the differences in Venus MFI were not caused by noise inherent to the microscopy procedure, we    The single cell analyses of Venus MFI showed higher amplitude and pulsatile changes in BCs and IAHCs than in HECs, indicating activated but unstable Gata2 expression as cells transit to hematopoietic fate. Because it was previously reported that Gata2 levels decrease during mitosis (Koga et al., 2007), we monitored our time-lapse videos for proliferation events. Although cell division was observed in 14% of IAHCs (Fig. S4), very few BCs (0.3%) and 0% of HECs divided (n = 15). As expected, Venus expression in IAHCs decreased during cell division. Because 3.5-fold more IAHCs (50%) showed fluctuating Venus expression than underwent cell division, and because no or very few proliferation events were detected in HECs and BCs during the imaging period, it is unlikely that cell division is responsible for the pulsatile behavior of Gata2 expression that we observe during the HEC to BC transition. To control for this, our results on Gata2 dynamic expression exclude cells undergoing mitosis.

Gata2 reporter pulse amplitude and periodicity distinguishes eHt subsets
Pulsatile behavior of regulatory molecules that relay information relevant to biological systems are characterized by their amplitude and periodicity of expression and/or activation state (Pourquié, 2011;Purvis and Lahav, 2013;Isomura and Kageyama, 2014). Expression amplitude is the maximal (peak) value a regulatory molecule attains during the observation period, whereas oscillation periodicity indicates the time between two adjacent peaks ( Fig. 4 A). To obtain the quantitative changes (trough-to-peak amplitude) in Venus expression, the fold change between the MFI at the peak and at the preceding trough was calculated. We developed an automated data-processing methodology to quantify amplitude and pulse periodicity in individually tracked cells (for details, see Materials and methods).
To discriminate noise from real Venus peaks, we set a threshold in which the peak Venus MFI differs from its neighboring minima by ≥15% of the mean intensity of the track. We could detect zero, one, two, and three peaks in Venus MFI profiles for individual EHT cells tracked over at least 10 consecutive time frames (Fig. 4 B). The majority of cells showed constant Venus MFIs (0 peaks, 57%) through the imaging period, and two peaks were found for 21% of cells (Fig. S3 E). To account for the difference in track lengths of the individual cells imaged, we normalized the pulse data and calculated the occurrence of peaks per 2.5 h (10 frames). Within the EHT subsets ( Fig. 4 C), a significantly higher percentage of HECs showed no peaks (68%) compared with BCs (56%: *, P < 0.05) and IAHCs (49%: ***, P < 0.001). Thus, Gata2 reporter expression showed a greater pulsatile behavior in BCs and IAHCs than in HECs (Fig. 4 C).
Because Gata2 dosage is known to be important for the normal production of IAHCs and functional HSPCs, the maximal fluorescent protein abundance reached within an expression pulse was also calculated. In individual cells, the mean Venus peak MFI was higher in BCs (53 ± 2.3) and IAHCs (51 ± 2.3) than in HECs (46 ± 1.5). Ventrally localized cells showed higher Venus peak MFIs than dorsal cells (Fig. 4 F). Interestingly, and in contrast to the peak MFIs, trough-to-peak amplitude measurements (Fig. 4 G) showed significantly higher fold changes in expression in BCs than in HECs (***, P = 0.0008) and IAHCs (***, P = 0.0008). 12.8% of ventral BCs and 1.5% of dorsal BCs showed a trough-to-peak amplitude higher than twofold, in contrast to 0.5% of ventral HECs and 0% dorsal HECs. In IAHCs (ventral and dorsal), only 6% of cells showed trough-to-peak amplitudes higher than twofold. Thus, the degree of trough-to-peak amplitude changes in Venus expression in BCs suggest that the upstream and downstream signals will be variable in this EHT subset and could provide an explanation for the known phenotypic/ functional heterogeneity of hematopoietic cells.
Molecular characterization of these cell fractions (RNA sequencing) revealed differential expression of 1,321 genes, of which 1,089 genes showed down-regulated expression and 232 genes up-regulated expression in the CD31 + ckit + V high fraction. The Ingenuity Pathway Analysis tool revealed that the 232 up-regulated differentially expressed genes in the CD31 + ckit + V high fraction were significantly overrepresented in canonical pathways expressed by mature myeloid cell types (innate immune; Fig. 5 C). In contrast, the CD31 + ckit + V med cells showed enrichment for genes involved in leukocyte extravasation and epithelial adherence junction pathways. These data support the functional data to indicate that CD31 + ckit + V high cells are more differentiated hematopoietic cells and the CD31 + ckit + V med cells are immature progenitors and stem cells. Considering that the Notch signaling pathway is involved in hematopoietic cell development and IAHC formation (Kumano et al., 2003;Guiu et al., 2013) and that Gata2 is a direct Notch target (Robert-Moreno et al., 2005), pathway-component analysis was performed. Sig- Figure 4. Pulse frequency and amplitude of Venus expression distinguishes eHt subsets. (A) Schematic representation of the automatic peak detection code. A local MFI maximum is considered a peak if it has at least a 15% higher intensity than its neighboring minima (see Materials and methods). The pulse period is the time between two adjacent peaks and the trough-to-peak amplitude the change between peak (highest value) and the preceding trough (lowest value). (B) Examples of normalized MFI profiles with no peak, one peak, and two pulse peaks showing increasing trough-to-peak amplitudes. (c) Distribution of the occurrence (percentage) of normalized pulse peak numbers in ECs, BCs, and IAHCs tracked over at least 10 consecutive frames (718 cells). To normalize for differences in track length, the data are presented as peaks per 10 frames (2.5 h) and represent the mean ± SEM (n = 15). Statistical significance was calculated using two-way ANO VA with Bonferroni post test (**, P < 0.01; ***, P < 0.001). (d) Distribution of the pulse periodicities of Venus + cells showing at least two pulse peaks (n = 15, 221 cells). (e) Distribution of the pulse periodicities in EHT subset cells showing at least two pulse peaks (n = 15, 86 HECs, 80 BCs, and 55 IAHCs). The data represent the mean ± SD. Statistical significance was calculated on the pooled data (n = 15) using two-way ANO VA with Bonferroni post test (**, P ≤ 0.01; ***, P < 0.001). (F and G) Peak intensity (F) and trough-to-peak amplitude (G) in the EHT cell subsets, plotted according to their ventral (v) or dorsal (d) location in the aorta (n = 13, cells showing at least one peak: 170 HECs, 151 BCs, and 65 IAHCs). The data represent the mean ± range. Statistical significance was calculated on the pooled data (n = 15) using Mann-Whitney U test (**, P = 0.0054; ***, P < 0.0008).
nificantly higher expression of Notch1 and its target gene, Hes1, was found in the CD31 + ckit + V med fraction as compared with the CD31 + ckit + V high fraction (Fig. 5 D), supporting a role for Notch in V med BCs and/or IAHCs. As expected, the heptad hematopoietic TF genes were expressed in both fractions (Fig. 5 E); expression of Erg, Fli1, and Lyl1 was lower in CD31 + ckit + V high cells, and expression of Lmo2, Runx1, and Scl was higher in CD31 + ckit + V high cells. These data indicate a degree of molecular heterogeneity within V + emerging hematopoietic cells.
To further examine whether Gata2 pulse periodicity, trough-to-peak amplitude, and hematopoietic functions in the aorta are related, we crossed G2V (G2 V/V ) and Gata2 +/− mice (Tsai et al., 1994; C-terminal zinc-finger deletion) to Overrepresentation of up-regulated differentially expressed genes (DEGs) in E10.5 CD31 + ckit + V med and CD31 + ckitV high sorted cells in IPA canonical pathways. (d and e) Mean FPKM values for genes in the Notch pathway (D) and heptad factor genes in E10.5 CD31 + ckit + V med and CD31 + ckitV high sorted E10.5 AGM cells (E). The data were compared using Student's t test (*, P = 0.0404; **, P = 0.0096). The data represent the mean ± SEM of three independent experiments. obtain embryos (Gata2 V/− ) with one mutated and one functional allele of Gata2 (Fig. S5 A). It is known that Gata2 heterozygous mutant embryos have a greatly reduced number of IAHCs, HPCs, and HSCs (Tsai and Orkin, 1997;Ling et al., 2004;Khandekar et al., 2007;de Pater et al., 2013;Gao et al., 2013), and HSCs are qualitatively defective (Ling et al., 2004;Rodrigues et al., 2005). As found by vital imaging (n = 6, 18 sections), the number of V + IAHCs and BCs was lower in Gata2 V/− aortas than in Gata2 V/+ aortas, whereas the number of V + HECs was similar between Gata2 V/− and Gata2 V/+ aortas (Fig. 6 A; compare Fig. S5 B with Fig. 3 A). Venus + BC and IAHC were almost exclusively on the ventral side. In line with our microscopy data, FACS analysis of Gata2 V/− aortas showed reduced numbers of CD31 + ckit + V + cells (Table 1). To examine Gata2 protein levels, we sorted E10.5 Gata2 V/+ and Gata2 V/− AGM cells into V + and V − fractions and performed Western blotting (Fig. S5, C and D). Equal levels of Gata2 protein were found in Gata2 V/+ and Gata2 V/− AGMs. Upon examination of the MFI values of all imaged V + cells in Gata2 V/− aortas ( Fig. 6 B), no differences in MFI were detected between Gata2 V/+ and Gata2 V/− HECs. However, in contrast to Gata2 V/+ aortas, where Venus expression was highest in IAHCs, Venus expression in Gata2 V/− aortas was highest in BCs. In the few remaining Gata2 V/− IAHCs, Venus expression was lower than in Gata2 V/+ IAHCs.
Further analysis of Gata2 reporter pulsatile expression parameters showed no difference in peak number distribution between Gata2 V/+ and Gata2 V/− HECs; in both cases, 30% of HECs showed pulsatile expression (Fig. S5 E). A trend toward reduced numbers of BCs with pulsatile expression was found in Gata2 V/− embryos (31%) as compared with Gata2 V/+ embryos (46%; Fig. S5 F). Despite no peak number differences in HECs, 51% of Gata2 V/− HECs showed pulse periodicities of ≤2 h as compared with 25% in Gata2 V/+ HECs (Fig. 6 C), indicating that bursts of Gata2 expression are more frequent in Gata2 V/− HECs. A similar trend toward reduced pulse periodicities was also found in Gata2 V/− BCs as compared with Gata2 V/+ BCs (Fig. 6 D). Because IAHC were highly reduced in Gata2 V/− aortas, we could not image sufficient numbers of IAHCs with pulsatile characteristics to reliably calculate periodicities. Within a pulse, Gata2 V/− HECs reached higher peak intensities (52.6 ± 2.7) than Gata2 V/+ HECs (45.8 ± 1.5, **, P = 0.0056; Fig. 6 E). BCs and IAHCs showed no peak MFI differences between Gata2 V/− and Gata2 V/+ cells. The fold increase in trough-to-peak amplitude (Fig. 6 F) in Gata2 V/− HECs did not change compared with Gata2 V/+ HECs. However, 11% of Gata2 V/+ BC showed trough-to-peak amplitudes higher than twofold, whereas only 4% of Gata2 V/− BCs showed values above twofold. Among the few Gata2 V/− IAHCs with at least one peak, the trough-to-peak amplitudes were similar to the values observed in Gata2 V/+ IAHCs. Together, our results show that Venus expression levels and pulsatile characteristics are altered during EHT in Gata2 heterozygous mutant embryos as compared with embryos with normal levels of Gata2 expression. dIscussIon We have uncovered a new level of dynamic regulation involving the pulsatile expression of the pivotal Gata2 TF during the establishment of hematopoietic cell fate in the embryo. Although genetic experiments implicate a role for Gata2 in EHT cell populations, vital imaging of G2V EHT cells reveals for the first time pulsatile expression at the single cell level. Pulse parameters, as characterized by amplitude and periodicity of Venus expression in individual cells differs between the EHT subsets (Fig. 6 G). The HEC to BC transition is accompanied by an increase in reporter expression levels and increased pulsatile behavior. Expression further increases and stabilizes during the transition to IAHCs, with the periodicity and amplitude decreasing. Our results suggest that the high degree of pulsatile Gata2 expression in BCs is linked to cell fate transition during EHT and may reflect an active process involving the partial assembly of counterbalancing regulatory states (Kueh et al., 2016). This is supported by pulsatile and level changes in Gata2 expression that accompany a Gata2 heterozygous mutant state in which EHT is disrupted.

Imaging dynamic cell transitions and Gata2 expression during eHt
Importantly, we used a G2V mouse model that does not disrupt Gata2 expression levels or the function of the Gata2 protein (Kaimakis et al., 2016). The recombination of an IRES-Venus fragment into the 3′ UTR avoids hypomorphic Gata2 expression and protein dysfunction that may result from a fusion protein. We showed previously that mice with two G2V alleles are normal in terms of HSC numbers and function. Gata2 protein has a relatively short half-life of 30-60 min Lurie et al., 2008) as compared with other hematopoietic TFs such as Runx1 (3.3 h;Lorsbach et al., 2004) and Gata1 (4 to >6 h; Minegishi et al., 2005;Lurie et al., 2008). Its instability is related to ubiquitination Lurie et al., 2008). The Venus reporter used in the G2V model has a half-life of ∼120 min (Li et al., 1998) and provides an excellent reporter of promoter activity, as it has a very short fluorescent protein formation (folding) time as compared with GFP (Snapp, 2009).
Our imaging (Fig. 2) and FACS (Table 1) experiments showed more cells undergoing EHT in the G2V embryonic aorta than previously described for the Ly6aGFP model (Boisset et al., 2010;Solaimani Kartalaei et al., 2015). At E10, Gata2 is expressed in ∼1,076 aortic ECs (CD31 + ckit − ) and ∼680 IAHC (CD31 + ckit + ), whereas Ly6aGFP is expressed in two to seven times fewer aortic . This is at the time when IAHCs peak and indicates that Gata2 expression marks more ECs with hemogenic potential and most if not all IAHCs. This is supported by functional data in which ∼80% of E11 AGM CFU-C are V + (Fig. 5 A), whereas only 33% are GFP + (Solaimani Kartalaei et al., 2015). However, all AGM HSCs are V + and GFP + (Solaimani Kartalaei et al., 2015). Thus, Ly6aGFP is a developmentally later marker, and Ly6aGFP-expressing cells are likely to represent a subset of Gata2-expressing cells that will have greater multilineage hematopoietic (including lymphoid) and HSC potential.

levels of Gata2
The exact relationship between Gata2 levels and cell fate decisions remains unclear. Previous work demonstrated that Gata2 dosage is important in regulating the quantity and functional quality of HSCs (Ling et al., 2004;Rodrigues et al., 2005;Tipping et al., 2009). It has been shown that Gata2 expression is down-regulated during lineage commitment (Orlic et al., 1995), suggesting a role for Gata2 in early hematopoietic progenitors and HSCs. More recently it has been shown that Gata2 lies at the core of a network of genes involved in lineage specification and mixed lineage states (Olsson et al., 2016). Transcriptome analysis of CD150 high adult Figure 6. Gata2 expression parameters, hematopoietic fate, and eHt are interrelated. (A) Maximum projections of confocal time-lapse images of E10.5 Gata2 V/+ and Gata2 V/− aortas immunostained with anti-CD31 (red; G2V, green). Bars, 40 µm. Gata2 V/+ and Gata2 V/− embryos were harvested from the same litter. Ventral side downward. (B) Venus MFI (averaged over frames 3-12) in single Gata2 V/-EHT subset cells (n = 6; 75 ECs, 37 BCs, and 12 IAHCs). The data were compared with Gata2 V/+ EHT subset cells using one-way ANO VA with Bonferroni post test (mean ± SD; *, P ≤ 0.024; **, P = 0.0085; ***, P = 0.0003). (c and d) Distribution of the pulse periodicities in Venus + EHT subset cells: HECs (C) and BCs (D) from E10.5 Gata2 V/− aortas showing at least two pulsatile peaks (n = 6; 18 HECs and 15 BCs). The data represent the mean ± SD. The data were compared with Gata2 V/+ HECs and BCs using two-way ANO VA with Bonferroni post test (**, P < 0.01). (e and F) Peak intensity (E) and trough-to-peak amplitude (F) in the Gata2 V/− EHT subsets (n = 6, cells showing at least one peak: 36 ECs, 20 BCs, and 6 IAHCs). The data represent the mean ± range. The data were compared with Gata2 V/+ EHT subset cells using Mann-Whitney U test (*, P = 0.0321; **, P = 0.0056). (G) Model of Gata2 expression dynamics and pulsatile characteristic during EHT. EHT cell types (top) are shown with accompanying Gata2 dynamic expression changes EHT directly below. G2V MFI (bright green) and pulse parameters (dark green sinusoids) are shown for Gata2 V/+ (middle) and Gata2 V/− (bottom) EHT subset cells.
bone marrow HSCs (Guo et al., 2013) showed concurrent high levels of Gata2 expression and Gata2 occupancy of megakaryocyte-erythroid lineage-related genes. These high expressing HSC showed a bias to the formation of more megakaryocyte-erythroid colonies.
We have shown previously in our G2V reporter mouse model that the Venus high-expressing fraction in the embryonic aorta contains differentiated basophilic cell types, myeloid cells and innate immune cells (Kaimakis et al., 2016). Here we confirm that HPCs can be found in the Venus high-expressing fraction and further show that all HSCs and a large number of HPCs are found in the Venus intermediate-expressing cell fraction. Thus, commitment to the myeloid lineage in the early embryo seems to be accompanied by increased levels of Gata2. In line with this, low-level overexpression studies in bone marrow HSCs using a tamoxifen-inducible Gata2 ERT construct (Tipping et al., 2009), mimicking physiological levels of Gata2 expression, promoted self-renewal and proliferation of myeloid progenitors. Physiological higher levels of Gata2 block lymphoid differentiation (Tipping et al., 2009) and negatively correlate with the occupation of Gata2 with lymphoid-related genes (Guo et al., 2013). Although these data suggest that Gata2 levels regulate commitment to specific hematopoietic lineages in the adult bone marrow, during development, when Gata2 expression is initiated and its expression is increasing in EHT cell populations, its levels are unstable in individual cells. Thus, we propose that Gata2 pulsatile expression (in combination with the onset/asynchronous expression and stability of other pivotal TFs such as Runx1; Kueh et al., 2016) is likely to play a role in the stochastic commitment to the hematopoietic lineage.

regulation of pulsatile expression behavior
At the transcriptional level, pathways such as Notch, β-catenin/Wnt, and fibroblast growth factor (Dequéant et al., 2006), form negative feedback loops with appropriate delay time for a pulsatile element to be translated and act at the starting point. The combination of negative and positive feedback loops prevents transcription from reaching a homeostatic steady state and maintains pulsatile expression (Purvis and Lahav, 2013). Transcription of Gata2 in the AGM is positively regulated by Notch1, which is required for EHT and HSC development (Robert-Moreno et al., 2005;Gama-Norton et al., 2015;Souilhol et al., 2016). Gata2 is autoregulatory and maintains its own transcription (Grass et al., 2003;Burch, 2005;Kobayashi-Osaki et al., 2005;Martowicz et al., 2005). Also, Notch1 activates Hes1, and Hes1 represses Gata2 expression specifically in AGM hematopoietic cells (Guiu et al., 2013). In the absence of Hes1, Gata2 expression is high (Guiu et al., 2013) and the number of cells in intra-aortic hematopoietic clusters is increased. However, persistent high-level Gata2 expression results in nonfunctional HSCs (Tipping et al., 2009).
The positive and negative signals induced by the Notch pathway result in a so-called type I incoherent feed-forward loop (Mangan and Alon, 2003). In the case of Gata2 in the embryonic aorta, we predict that the Notch-Hes1-Gata2 feed forward loop is responsible for the pulsatile expression of G2V that we observed in the EHT cell subsets: Notch1 would stimulate Gata2 and Hes1 transcription, and Gata2 transcription would be repressed when Hes1 protein reaches a critical threshold (half-life, 24 min; Yoshiura et al., 2007), resulting in a pulse-like dynamics of Gata2 protein levels when Hes levels subsequently drop. That Hes1 is the likely pacemaker of Gata2 pulsatile expression is supported by our RNA-sequencing data showing that Hes1 is fourfold and Notch1 sixfold up-regulated in the CD31 + ckit + Venus med fraction, as compared with the CD31 + ckit + Venus high fraction. Moreover, the higher expression of Gata2 compared with Hes1 in the CD31 + ckit + Venus high fraction suggests that critical Notch signaling thresholds will impact Gata2 expression parameters in BCs versus IAHCs.
The in vivo G2V reporter allows for the first time the unbiased real-time characterization of Gata2 expression during EHT in the Gata2 WT and heterozygous mutant state. The mutant Gata2 mouse model has a deletion of the second zinc-finger domain in the Gata2 gene (Tsai et al., 1994), leaving Gata2-binding motifs available on both alleles. Therefore, the altered Gata2 pulsatile expression behavior in heterozygous mutant ECs and BCs cannot be explained by more Notch1 and Hes1 binding to only one allele of Gata2. Only half of the dose of DNA-binding Gata2 protein would be available to bind two alleles of Gata2, strongly suggesting that a reduced positive autoregulation manifests itself in altered Gata2 dynamic expression. As yet, we do not have a direct correlation between the levels of Venus protein and Gata2 protein in single cells of the AGM. In the future, mass spectrometry CyTOF (Giesen et al., 2014) could be used to more specifically address this issue. Further in vivo vital molecular studies, coupled with computational modeling of the interplay of Gata2 regulators will be needed for a detailed understanding of the molecular basis of pulsatile expression. Given the fact that in hematopoietic disorders and malignancies GATA2 mutations occur in the second zinc finger (Bresnick et al., 2012), the dysregulation of Gata2 pulsatile expression through a feed-forward loop might provide a mechanistic basis for human hematologic pathophysiologies.

time-lapse imaging and detection of Gata2 dynamics
Aortic transversal sections of E10 G2V embryos were prepared as previously described (Boisset et al., 2010). Briefly, nonfixed E10 (32-37 SP) embryos were freed from placenta, yolk sac, amnion, and head. Antibodies against CD31 and ckit (diluted in PBS/10% FCS/1% PS) were directly injected into the embryonic aorta. Transversal aortic slices of 150 µm width were cut with a tissue chopper (McIlwain). Draq5 (BioLegend) staining was performed on transversal G2V sections (15 min, RT, diluted in PBS/10% FCS/1% PS), after which sections were washed twice. Selected sections (from trunk to hindlimb) were subsequently embedded in 1% agarose in PBS and after polymerization overlaid with myeloid long-term culture medium (MyeloCult; StemCell Technologies) containing hydrocortisone and IL-3. Confocal time-lapse imaging was performed using a Leica SP5 microscope, equipped with 405-nm, argon, 561-nm, and 633-nm laser lines using a 20×, 0.7-NA air objective and typically a pinhole of 1-1.5 AU. Videos were recorded at a time interval of 15 min for a total of 12-15 h. For each experiment, three to five aorta slices, with a z-range of 20-50 steps (step size, 0.7-2.5 µm) were imaged. The sample temperature was maintained by a stage heater (37°C) and the sample was kept under constant CO 2 levels (5%). The G2V signal was collected using an avalanche photo diode (APD) with a BP 535-585 emission filter, whereas the CD31-AF647 signal was collected with a photomultiplier tube (PMT) and a BP 650-720 emission filter. ckit-DyLight405 or ckit-BV421 were typically only imaged at the first frame of the time-lapse imaging series and detected with a BP 420-480 emission filter. To ensure that the Venus MFI was comparable between experiments, the microscope settings (laser power, gain, and settings of the emission filters) were kept similar among experiments.

Image processing
To improve signal-to-noise ratios for more accurate tracking and object recognition, time-lapse imaging series were deconvolved using the Huygens Professional (Scientific Volume Imaging) Deconvolution Wizard. Small drifts in z and xy were corrected by the Huygens Professional Object Stabilizer. Deconvolved and stabilized time series were used for further analysis.

Quantification of Gata2 dynamics
To analyze the dynamics of Venus expression in single cells in the aorta, Venus + cells had to be tracked and the Venus fluorescence signal corresponding to individual single cells had to be extracted. Because no commercial tool was available that reliably tracked Venus expressing cells in the aorta and at the same time extracted the fluorescent signal, we developed a custom-made code to combine two commercial tools (1) tracking Venus + cells (Huygens Professional Object Tracker) and (2) extracting voxel information (Huygens Professional Object Analyzer) of the Venus fluorescent signals in three dimensions and in time. Object Tracker and Object Analyzer use different algorithms to track and segment objects; therefore each tool assigned a unique identifier to each object (cell). Because both tools use the center of mass to describe the position of the object, our LabVIEW-based custom-made code assigned to each tracked object the closest segmented object with voxel information (within a maximum range of 5 µm). The resulting Venus + cells with common identifier could be visualized in each time-lapse series by a custom-written Fiji macro. The tracked cells were visually inspected, and incorrectly tracked cells were excluded from further analysis. Moreover, further analysis was limited to cells that could be tracked over at least 10 consecutive frames and did not show any bleaching or overall intensity changes caused by the microscope setup.
To quantify the dynamics of Venus expression, the Lab-VIEW code also computed the volumetric MFI values of each tracked cell, which was defined as the sum of all intensities divided by the number of voxels representing the cell (Fig. S2). For further analysis, MAT LAB codes (version 2015b) were developed to plot the Venus MFI as a function of time (Fig. S2). As a control, the MFI values were plotted against the voxel values, confirming that quantitative changes in the fluorescent intensity were not due to tracking errors.

data analysis
To assess whether the Venus signal in the time series data undergoes quantitative changes, we adapted a publicdomain MAT LAB code (http ://www .billauer .co .il /peakdet .html) to automatically detect significant extrema in our Venus MFI time series data (Todd and Andrews, 1999). To discriminate against "noise" (such as fluctuations introduced by imperfections of the image stack segmentation), the code only considered local maximum as significant if it differed from its neighboring minima by more than a predefined threshold (specified as a percentage of the mean intensity of the track). For the analysis in this article, we used a threshold of 15%. Visual inspection of the minima and maxima confirmed that ∼90% of peaks were correctly detected using this threshold. Tracks with incorrectly recognized peaks were excluded from further analysis. Additional codes calculated the number of peaks, oscillation periodicity, peak minimum and peak maximum, and trough-to-peak amplitude. The data were exported from MAT LAB to excel for further analysis and plotted in GraphPad Prism 5.

Hematopoietic assays
The methylcellulose colony-forming assay was performed as previously described . CD31 + ckit + V med and CD31 + ckit + V high sorted E10 AGMs (including part of the vitelline and umbilical arteries) were seeded in triplicate in methylcellulose (1 ml per dish; M3434; Stem Cell Technology) with 1% PS and incubated for 10 to 12 d at 37°C, 5% CO 2 . Colonies were counted with a brightfield microscope. Transplantation experiments were performed as previously described . Sorted CD31 + ckit + V − , CD31 + ckit − V + , CD31 + ckit + V med , and CD31 + ckit + V high (Ly5.2/Ly5.2) cells of five to seven E11 AGMs were transplanted into 9.5-Gy irradiated (Ly5.1/Ly5.1) recipients together with 2 × 10 5 spleen cells from the recipient strain. Peripheral blood was analyzed by flow cytometry for donor contribution by anti-Ly5.1/anti-Ly5.2 labeling 1 and 4 mo after transplantation. Transplanted recipients were scored as positive if the peripheral blood donor chimerism was ≥10%. Multilineage organ chimerism analysis (lymphoid and myeloid) was performed 4 mo after transplantation. rnA isolation mrnA-sequencing analysis CD31 + ckit + V med and CD31 + ckit + V high E10.5 AGM cells of G2 V/+ embryos were sorted into PBS/50% FCS/1% PS. After centrifugation and removal of supernatant, cells were lysed, and RNA was isolated using the mirVana miRNA Isolation kit (Ambion) according to the manufacturer's protocol. RNA quality and quantity were accessed by the 2100 Bioanalyzer (Picochip; Agilent Technologies). RNA samples were prepared by SMA RTer protocol. Illumina TrueSeq v2 protocol was used on HiSeq2500 with a single-read 50-bp and 9-bp index. Reads were aligned to the mouse genome (GRCm38/mm10) using Tophat/Bowtie, and the generated count table was analyzed by R/Bioconductor package edgeR according to McCarthy et al. (2012). Counts were normalized for mRNA abundance, and differential expression analysis was performed using edgeR. The B-H method was used for p-value correction with a false discovery rate of 0.05 as statistically significant. Variance stabilized counts were calculated by R/Bioconductor package DESeq for all genes (Anders and Huber, 2010). Cufflinks was used to compute transcript abundance estimates in fragments per kilobase per million (FPKM; Trapnell et al., 2013). For differentially expressed genes, the FPKM for each gene across all samples were normalized by division with maximum FPKM observed for that gene. Differentially expressed genes were analyzed for the top five most enriched Ingenuity Pathway Analysis pathways against a background of all mouse genes by right tailed Fisher exact tests in a core analysis calculating the likelihood that this is due to random chance. The accession number for the RNA-sequencing data is Gene Expression Omnibus: GSE106072. sds-PAGe and Western blot 4 × 10 4 E10 Venus med and Venus high AGM cells were sorted from E10.5 G2 V/+ and G2 V/− embryos (littermates), washed and centrifuged, and directly lysed in Laemmli sample buffer. 4.5 to 6.2 × 10 4 G2 V/V bone marrow mononuclear cells were sorted into Venus − , Venus med , and Venus high cell fractions, subsequently lysed in RIPA buffer plus protease and phosphatase inhibitor, and sonicated. Then Laemmli buffer was added. Proteins were separated by SDS-PAGE and transferred to PVDF membranes (Millipore). Subsequently, proteins were detected by anti-Gata2, anti-Venus, anti-GAP DH, anti-β-actin, anti-Hsp90, and anti-Cohesin immunoblotting. After labeling, Western blots were scanned using the Odyssey imager (LI-COR Biosciences).

statistics
The data were compared in GraphPad Prism 5 using Mann-Whitney U tests, Student's t tests, and one-or two-way ANO VA with Bonferroni post test, as indicated. Errors in the frequency of oscillation periodicity were estimated by bootstrapping (resampling residuals approach). Error bars represent two times the standard deviation originated from fitting procedures. online supplemental material Fig. S1 shows a Western blot of sorted Venus med and Venus high cells, demonstrating that Venus protein levels correctly reflect levels of Gata2, and confocal images show the differential expression pattern of Gata2 and Ly6a in Ly6aGFP :G2V thick aortic sections at E10.5. Fig. S2 illustrates the image acquisition and processing pipeline to analyze Venus expression in embryonic sections during confocal time-lapse imaging. Fig. S3 shows the visualization of Venus expression peaks throughout the imaging session. Fig. S4 shows two examples in which Venus expression IAHCs undergo mitosis during the imaging session. Fig. S5 shows Gata2 expression characteristics in Gata2 heterozygous mutant embryos. Videos 1, 2, and 3 show examples of Venus-expressing cells undergoing EHT during G2V time-lapse imaging.