The brain contains hundreds of types of neurons, which differ in size, shape and behavior. But neuroscientists often wish to study individual neuronal types in isolation. They are able to do this with the aid of a toolkit made up of two parts: viral vectors and genetically modified mice.

Viral vectors are viruses that have been modified so that they are no longer harmful and can instead be used to introduce genetic material into cells on demand. To create a viral vector, the virus’ own genetic material is replaced with a ‘cargo’ gene, such as the gene for a fluorescent protein. The virus is then introduced into a new host such as a mouse. Importantly, the virus only produces the protein encoded by its ‘cargo’ gene if it is inside a cell that also contains one of two specific enzymes. These enzymes are called Cre and Flp.

This is where the second part of the toolkit comes in. Mice can be genetically engineered to produce either Cre or Flp exclusively in specific cell types. By introducing a viral vector into mice that produce either Cre or Flp only in one particular type of neuron, researchers can limit the activity of the cargo gene to that neuronal type.

But sometimes even this approach is not selective enough. Researchers may wish to limit the activity of the cargo gene to a subpopulation of cells that produce Cre or Flp. Or they may wish to target only Cre- or Flp-producing cells in a small area of the brain, while leaving cells in neighboring areas unaffected.

Sabatini et al. have now overcome this limitation by developing and testing a new set of viral vectors that are active only in neurons that produce both Cre and Flp. The vectors are called tTARGIT AAVs and allow researchers to target cells more precisely than was possible with the previous version of the toolkit.

Sabatini et al. show tTARGIT AAVs in action by using them to identify a group of neurons that control how much energy mice use and how much food they eat. As well as applying the vectors to their own research on obesity, Sabatini et al. have also made them freely available for other researchers to use in their own projects.

The molecular heterogeneity of the nervous system requires a rich toolkit for precise study of distinct cell populations. Together with Cre recombinase-expressing mice, the available suite of Cre-dependent viral vectors permits the manipulation of genetically identified neural types. However, this approach neither permits the study of subpopulations of cells expressing a particular Cre allele (Hodge et al., 2019; Mickelsen et al., 2019) nor permits the study of Cre-expressing cells within a defined Central nervous system (CNS) site while excluding the Cre-expressing cells in closely apposed brain regions.

Restricting transgene expression to cells that express two marker genes can overcome this challenge (Luan and White, 2007). Early attempts at this approach utilized gene-specific, Cre-sensitive transgene-expressing alleles in combination with Cre alleles driven by distinct genes (Chen et al., 2011). Another solution involves expressing two Cre fragments across different transgenes with distinct promoters, reconstituting active Cre only in cells that express both pieces (Hirrlinger et al., 2009). These approaches generally involve substantial investments of time and resources, however, as they require generating and interbreeding multiple new gene- and experiment-specific alleles.

Transgene expression can also be directed to cell types defined by two marker genes through the use of recombinase-dependent alleles (often inserted into the Rosa26 Locus) containing a ubiquitous promoter and tandem STOP cassettes that are each excised by distinct recombinases (Daigle et al., 2018; Sciolino et al., 2016).

Neuroscience research often requires a higher degree of spatial specificity than afforded by the intersectional genetic models outlined above. Stereotaxic injections of recombinase-sensitive viral vectors can restrict transgene expression to a narrow anatomical region. Adeno-associated viruses (AAVs) generally represent the preferred viral system for Cre-dependent transgene expression, given their minimal toxicity and the speed and ease of their generation. Developing AAVs sensitive to multiple recombinases has been challenging because of the limited AAV genome size, which precludes the use of multiple recombinase-sensitive STOP cassettes (Wu et al., 2010).

The INTRSECT system overcomes this limitation by utilizing a single AAV vector that flanks the transgene coding sequence with lox and FRT sites in such a way that combinatorial expression of Cre and Flp permits expression of a functional pre-mRNA that can then be spliced to produce a mature coding sequence (Fenno et al., 2020; Fenno et al., 2014). The multiple inversion and splicing steps involved in this system can limit transgene expression, however, perhaps due to the relatively poor recombinase activity of Flp (and even optimized FlpO). Furthermore, generating INTRSECT viruses that express new transgenes requires a relatively complex and labor-intensive design and optimization process (Fenno et al., 2020; Fenno et al., 2017).

Seeking a dual recombinase-activated AAV system to overcome these limitations and that could be modified quickly and easily, we generated tetracycline transactivator (tTA)-driven Recombinase-Guided Intersectional Targeting (tTARGIT) AAVs composed of a Flp-dependent tetracycline transactivator (tTA) ‘Driver’ AAV and a tetracycline response element (TRE)-driven Cre-dependent ‘Payload’ AAV to express the transgene of interest.

We applied tTARGIT AAVs to the study of ventromedial hypothalamic LepRb (VMH; LepRb^VMH) neurons, which modulate metabolic adaptations to obesogenic diets (Bingham et al., 2008; Dhillon et al., 2006) but have proven difficult to study directly due to the density and proximity of neighboring LepRb populations. Together with Lepr^Cre and Slc16a7^FlpO, tTARGIT AAVs allowed us to overcome these challenges and reveal a specific role for LepRb^VMH neurons in suppressing food intake and increasing energy expenditure to promote weight loss.

The density of LepRb neurons in the adjacent arcuate nucleus (ARC), dorsomedial hypothalamus, and lateral hypothalamic area complicated our initial attempts to study LepRb^VMH neurons using Lepr^Cre mice and Cre-dependent vectors; viruses targeted to the VMH spread to other nearby Lepr^Cre neurons, confounding the interpretation of our results (data not shown). Because Slc17a6, encoding the vesicular glutamate transporter 2 (vGLUT2) protein, expression is largely restricted to LepRb^VMH neurons and absent from most surrounding LepRb cells (Vong et al., 2011), we generated a Slc17a6^FlpO strain and crossed it with Lepr^Cre and a novel Flp- and Cre-dependent reporter (Rosa26^{RCFL-eGFP-L10a}). We then tested the potential ability of this combination of Cre and Flp alleles to specify LepRb^VMH neurons in the mediobasal hypothalamus. Although these reporter mice identified Cre- and Flp-co-expressing LepRb^Slc17a6 neurons elsewhere in the brain, within the mediobasal hypothalamus this approach largely limited eGFP expression to VMH cells (Figure 1—figure supplement 1).

We thus sought to use Flp- and Cre-dependent AAVs to target LepRb^VMH neurons and omit manipulation of non-VMH LepRb^Slc17a6 cells. We injected the INTRSECT AAV system (Fenno et al., 2014) into the VMH of Slc17a6^FlpO;Lepr^Cre mice in an attempt to express channelrhodopsin (ChR2) in LepRb^VMH cells. However, this approach resulted in detectable ChR2 expression in one or fewer cells per section (Figure 1—figure supplement 2). We surmised that while INTRSECT works well in systems with robust Flp and Cre activities, the poor recombinase activity of Flp and the more moderate Flp and Cre expression mediated by Slc17a6^FlpO and Lepr^Cre, respectively, might limit INTRSECT-mediated transgene expression in LepRb^VMH cells.

We therefore set out to develop a more sensitive AAV system to drive Cre+Flp-dependent transgene expression, using as our framework a previously described inducible gene expression system based on recombinase-dependent expression of the tetracycline transactivator (tTA) in combination with a TRE-driven transgene-expressing allele (Chan et al., 2017; He et al., 2016). We packaged this system into two viral vectors, hereafter ‘tTA-driven Recombinase-Guided Intersectional Targeting’ (tTARGIT) AAVs.

Our tTARGIT system utilizes ‘Driver’ and ‘Payload’ AAVs. The Driver (AAV-hSYN1-fDIO-tTA) utilizes Flp-dependent Double-Floxed Inverted Open reading frame (fDIO) cassette (Fenno et al., 2014) to Flp-dependently invert tTA, permitting its expression by a human synapsin I (hSYN1) promoter. This virus also contains two tetracycline operators (TetO) to drive a positive feedback loop and increase tTA expression (Chan et al., 2017). The Payload AAV mediates tTA/TRE-dependent transgene expression following its Cre-mediated inversion into the sense orientation. Hence, only cells that contain both recombinases express the transgene (Figure 1a). Tetracycline inhibits tTA-dependent gene expression (Das et al., 2016), so this system mediates constitutive payload expression in target cells in the absence of tetracycline.

Figure 1 with 5 supplements see all

Download asset Open asset

To test the recombinase dependence of this system, we combined the Flp-dependent Driver AAV and a Payload AAV that permits the tTA/TRE-mediated Cre-dependent expression of a ChR2-TdTomato fusion protein (ChR2-TdT; AAV-TRE-DIO-ChR2-TdT). We co-injected these viruses into the VMH of wild-type mice, mice that expressed either Slc17a6^FlpO or Lepr^Cre only, or Slc17a6^FlpO;Lepr^Cre mice (Figure 1b,c). We detected no TdT (DSRed-immunoreactivity [IR]) in the VMH of wild-type or Slc17a6^FlpO animals and minimal expression in Lepr^Cre mice (Figure 1—figure supplement 3). In contrast, the Driver+Payload combination mediated robust DSRed-IR in the VMH of Slc17a6^FlpO;Lepr^Cre mice (Figure 1c); furthermore, VMH photostimulation in these mice promoted robust colocalization of DSRed- and FOS-IR, consistent with the ability of this system to activate transduced Flp+Cre-expressing cells (Figure 1—figure supplement 4).

We also tested the requirement for both the Driver and Payload viruses in this system by injecting each virus alone, or both viruses together, into the VMH of Slc17a6^FlpO;Lepr^Cre animals (Figure 1d). As expected, injecting either virus alone produced minimal or no detectable DSRed-IR, while co-injection of the two viruses yielded robust DSRed-IR (Figure 1e). Thus, robust transgene expression by the tTARGIT system requires injection of both the Driver and Payload AAVs, as well as the presence of both Flp and Cre recombinases.

The tTARGIT approach can also be modified from a Flp-ON/Cre-ON system, requiring both Flp and Cre recombinases for payload expression, to a Flp-ON/Cre-OFF system, mediating transgene expression in all Flp-expressing cells that do not contain Cre (Figure 1—figure supplement 5; Table 1). Placing the payload transgene in the forward orientation within the DIO cassette permits tTA-driven transgene expression in all Flp-expressing cells that do not contain Cre because Cre inverts the Payload transgene into the antisense orientation. We tested this system with a novel Cre-inactivated Payload virus expressing a hM3Dq designer receptor exclusively activated by designer drugs (DREADD)-mCherry transgene. We co-injected this Cre-OFF Payload and the Flp-dependent Driver AAV into the VMH of Slc17a6^FlpO;Lepr^Cre animals on the Cre-dependent Rosa26^LSL-GFP-L10a background (Figure 1—figure supplement 5b). As expected, this modified tTARGIT system drove hM3Dq-mCherry expression almost exclusively in cells that did not express the Cre-dependent GFP (Figure 1—figure supplement 5c). We surmise the few GFP-IR neurons with detectable mCherry, constituting 1–7% of all mCherry cells, might result from the low Cre expression mediated by Lepr^Cre (Patterson et al., 2011) and predict that the Flp-On, Cre-Off tTARGIT system should demonstrate complete Payload inactivation when used in conjunction with a more robustly expressing Cre allele or require longer incubation periods to fully inactivate the payload transgene.

To define the projection targets of LepRb^VMH neurons, we developed a payload virus (AAV-TRE-DIO-GFP-2A-SynmRuby) that encodes GFP plus a cotranslationally expressed synaptophysin-mRuby transgene (Figure 2a). Co-injection of this tTARGIT Payload AAV and the Driver virus into the VMH of Slc17a6^FlpO;Lepr^Cre mice promoted robust VMH-restricted GFP-IR (Figure 2b). mRuby detection (DSRed-IR) for this virus was much lower than for GFP, however (data not shown); thus, we used GFP-IR to detect projections from LepRb^VMH cells. Assessing the entire CNS for the presence of GFP-IR revealed terminals in the periaqueductal gray, the arcuate, the periventricular thalamic nucleus, the periventricular hypothalamic nucleus, the bed nucleus of the stria terminalis, and the preoptic area (Figure 2c,d). These are consistent with the known projections of the VMH (Canteras et al., 1994; Meek et al., 2016; Zhang et al., 2020).

Figure 2

Download asset Open asset

Ablating Lepr expression from the VMH (Nr5a1^Cre-mediated) promotes obesity associated with decreased energy expenditure in high-fat-diet-fed animals, suggesting a specific role for LepRb signaling in LepRb^VMH cells in the control of energy balance via the dietary modulation of energy utilization (Bingham et al., 2008; Dhillon et al., 2006). To define the function of LepRb^VMH cells, rather than the function of LepRb in these cells, we developed a Payload virus containing an inverted hM3Dq-mCherry transgene. We co-injected the Driver AAV and this AAV-TRE-DIO-hM3Dq-mCherry Payload virus into the VMH of Slc17a6^FlpO;Lepr^Cre animals (LepRb^VMH-Dq mice; Figure 3a). This approach promoted robust VMH-restricted expression of functional hM3Dq-mCherry as administration of the DREADD activator (Pei et al., 2008), clozapine-N-oxide (CNO), stimulated colocalization of FOS with DSRed-IR cells (Figure 3b–d).

Figure 3

Download asset Open asset

Figure 3—source data 1

Raw Data from metabolic cages.

https://cdn.elifesciences.org/articles/66835/elife-66835-fig3-data1-v2.xlsx

Download elife-66835-fig3-data1-v2.xlsx

To determine the potential modulation of energy expenditure, activity, and food intake by LepRb^VMH neuron activation, we placed LepRb^VMH-Dq animals in metabolic cages and administered either vehicle or CNO twice daily (Figure 3e–j). Compared to vehicle administration, activating LepRb^VMH neurons significantly increased 24 hr oxygen consumption (VO₂) and energy expenditure, both primarily due to effects during the light phase, despite decreasing ambulatory activity over 24 hr (primarily due to effects during the dark phase) (Figure 3g,h). Additionally, the hM3Dq-mediated activation of LepRb^VMH neurons also suppressed 24 hr food intake, primarily due to decreased light-phase feeding, revealing a previously unsuspected role for these cells in the suppression of feeding. CNO also decreased the respiratory exchange ratio during the light phase, consistent with the increased metabolism of fat stores due to the combination of increased energy expenditure and decreased food intake.

To understand whether effects on brown adipose tissue (BAT) might contribute to the increased energy expenditure during LepRb^VMH neuron activation, we placed temperature probes in the interscapular space of LepRb^VMH-Dq animals to monitor BAT thermogenesis. Compared to controls, CNO significantly increased intrascapular temperatures in LepRb^VMH-Dq animals, suggesting LepRb^VMH neurons promote energy expenditure at least in part by augmenting BAT thermogenesis (Figure 3k–l).

As activating LepRb^VMH neurons increased energy expenditure and decreased food intake, we surmised that these neurons should promote weight loss. We thus administered CNO in drinking water to LepRb^VMH-Dq mice or Lepr^Cre-only control animals (lacking any Flp expression) injected with the tTARGIT hM3Dq for 3 days. During this time, the body weight of LepRb^VMH-Dq mice decreased by approximately 10% (Figure 4a), returning to baseline following the cessation of CNO exposure. Importantly, body weight of Lepr^Cre-only controls remained stable. While CNO treatment decreased food consumption (largely during the second day of treatment) and water intake (Figure 4b–d),the magnitude and timing of these ingestive effects dictates that neither could account for the decreased body weight mediated by CNO, consistent with the notion that increased energy expenditure mediates the major effect of LepRb^VMH cells on body weight.

Figure 4

Download asset Open asset

Figure 4—source data 1

Raw data from chronic CNO dosing.

https://cdn.elifesciences.org/articles/66835/elife-66835-fig4-data1-v2.xlsx

Download elife-66835-fig4-data1-v2.xlsx

Hence, the use of our dual recombinase-dependent tTARGIT AAV system permitted us to determine that LepRb^VMH neuron activation increased energy expenditure and decreased food intake during the inactive phase, suggesting the diurnal control of energy balance by LepRb^VMH neurons.

The use of sequence-specific DNA recombinases (Cre, Flp, and others) in conjunction with recombinase-dependent genetic alleles and viral vectors has revolutionized our ability to manipulate specific circuits and understand the central nervous system. The lack of robust viral systems to manipulate cell populations defined by the expression of multiple genes has impeded the study of more refined neural populations, however, including those identified by single cell RNA-sequencing (Campbell et al., 2017). Our tTARGIT AAV system addresses many shortcomings of previous intersectional tools, including limitations to transgene expression and the difficulty of incorporating novel transgenes into the AAV plasmids.

As the tTARGIT system is based on the use of dual AAVs, it is possible that the two viruses could compete with each other for the surface receptors required for viral internalization and/or for the cellular machinery required for transgene production, thereby reducing expression efficiency from both AAVs. Based upon our results, and since dual AAVs have been used successfully previously (Akil et al., 2019; Xu et al., 2018; Yang et al., 2016) and transduce the same cells in vivo (Vardy et al., 2015), we surmise that if such competition exists with tTARGIT AAVs, it is minor.

To further facilitate the study of intersectional neural populations, we developed a suite of Cre-dependent tTARGIT Payload plasmids (Table 1). While we have not yet tested the function of all of these, our experience with the transgenes that we have tested predicts similarly robust expression of the various Payload AAV transgenes. The limitations of these Payload vectors are likely to mirror those of standard viruses with Cre-dependent transgenes, including the requirement for stoichiometric transduction of/recombination in the cell type of interest to observe the effects of interfering with neuronal function.

Additionally, the tTARGIT system displays some detectable payload expression, albeit at low levels and in a small number of cells, in Lepr^Cre-only mice co-injected with Driver and Payload AAVs, as well as in Slc17a6^FlpO;Lepr^Cre mice injected with the Payload AAV alone. We surmise this results from the previously noted tTA-independent activity of the TRE (Das et al., 2016). We expect this tTA-independent Payload transgene expression to predominately occur in Cre-expressing cells, which have inverted the transgene into the correct orientation downstream of the TRE. In our studies, leaky payload expression had little impact as the expression levels were low and failed to produce a metabolic response to CNO in drinking water. Nevertheless, the possibility of tTA-independent payload expression represents an important consideration for experimental design and requires the inclusion of appropriate controls.

In addition to Cre-ON/Flp-ON tTARGIT plasmids, we also generated Cre-inactivated Payload vectors to specifically mark Cre-negative Flp-expressing cells within the injection field. Within our model, we found up to 7% of mCherry-IR cells were co-labeled with the Cre-dependent GFP reporter. We posit that increasing post-injection incubation times prior to study or using alleles with higher Cre expression than observed in Lepr^Cre may further reduce the number of Cre-labeled cells expressing the Cre-OFF tTARGIT payload. Hence, inclusion of appropriate control will be required for studies employing tTARGIT AAVs.

While the tTARGIT AAV system as we have built it is designed to constitutively express transgenes in Cre- and Flp-expressing cells without the use of tetracycline-like compounds (usually doxycycline), it should be possible to decrease transgene expression from the tTARGIT AAVs by doxycycline treatment. Indeed, we built a Flp-dependent rtTA Driver virus that is predicted to require doxycycline treatment to mediate strong transgene expression. The rtTA-based system can drive low-level transcription independent of doxycycline (Zhu et al., 2001), however, and we have not yet tested this system.

The use of tTARGIT AAVs permitted us to target robust transgene expression to LepRb^VMH neurons specifically, in isolation from LepRb neurons in adjacent hypothalamic nuclei. While deleting Lepr from the VMH of chow-fed mice or restoring VMH Lepr expression on an otherwise LepRb-deficient background minimally (if at all) alters energy balance, knockout mice fail to increase energy expenditure on high-fat chow and become more obese than controls (Bingham et al., 2008; Dhillon et al., 2006; Gonçalves et al., 2014; Senn et al., 2019). These studies thus suggest a role for leptin action on LepRb^VMH cells in the control of energy expenditure, but were unable to study LepRb^VMH cells more broadly. In contrast, our use of the tTARGIT system, together with Slc17a6^FlpO;Lepr^Cre mice, identified the projections of LepRb^VMH cells, demonstrated their ability to acutely suppress food intake as well as promoting energy expenditure (identifying BAT thermogenesis as a target for these cells), and revealed the diurnal nature of LepRb^VMH neuron-mediated control of energy balance. Presumably, the finding that LepRb^VMH neuron activation alters food intake and energy expenditure specifically during the light cycle suggests that these neurons may decrease in activity during the inactive/light phase, permitting us to observe the effects of artificial neuron activation during this time.

In summary, we have developed a suite of dyad AAV vectors for the study of intersectional neural populations marked by the co-expression of Flp and Cre or marked by expression of Flp in the absence of Cre. This tTARGIT system yields robust dual recombinase-sensitive expression of the desired payload in vivo. With this approach, we defined the neural circuitry and functional capacity of LepRb^VMH neurons. These intersectional genetic tools will facilitate the study of a broad range of dual gene-defined cell populations across the central nervous system.

All data generated or analysed during this study are included in the manuscript and supporting files.

1. 1. Akil O
   2. Dyka F
   3. Calvet C
   4. Emptoz A
   5. Lahlou G
   6. Nouaille S
   7. Boutet de Monvel J
   8. Hardelin JP
   9. Hauswirth WW
   10. Avan P
   11. Petit C
   12. Safieddine S
   13. Lustig LR

   (2019) Dual AAV-mediated gene therapy restores hearing in a DFNB9 mouse model

   PNAS 116:4496–4501.

   https://doi.org/10.1073/pnas.1817537116

   • PubMed
   • Google Scholar
2. 1. Allison MB
   2. Patterson CM
   3. Krashes MJ
   4. Lowell BB
   5. Myers MG
   6. Olson DP

   (2015) TRAP-seq defines markers for novel populations of hypothalamic and brainstem LepRb neurons

   Molecular Metabolism 4:299–309.

   https://doi.org/10.1016/j.molmet.2015.01.012

   • PubMed
   • Google Scholar
3. 1. Balthasar N
   2. Dalgaard LT
   3. Lee CE
   4. Yu J
   5. Funahashi H
   6. Williams T
   7. Ferreira M
   8. Tang V
   9. McGovern RA
   10. Kenny CD
   11. Christiansen LM
   12. Edelstein E
   13. Choi B
   14. Boss O
   15. Aschkenasi C
   16. Zhang CY
   17. Mountjoy K
   18. Kishi T
   19. Elmquist JK
   20. Lowell BB

   (2005) Divergence of melanocortin pathways in the control of food intake and energy expenditure

   Cell 123:493–505.

   https://doi.org/10.1016/j.cell.2005.08.035

   • PubMed
   • Google Scholar
4. 1. Bingham NC
   2. Anderson KK
   3. Reuter AL
   4. Stallings NR
   5. Parker KL

   (2008) Selective loss of leptin receptors in the ventromedial hypothalamic nucleus results in increased adiposity and a metabolic syndrome

   Endocrinology 149:2138–2148.

   https://doi.org/10.1210/en.2007-1200

   • Google Scholar
5. 1. Campbell JN
   2. Macosko EZ
   3. Fenselau H
   4. Pers TH
   5. Lyubetskaya A
   6. Tenen D
   7. Goldman M
   8. Verstegen AM
   9. Resch JM
   10. McCarroll SA
   11. Rosen ED
   12. Lowell BB
   13. Tsai LT

   (2017) A molecular census of arcuate hypothalamus and median eminence cell types

   Nature Neuroscience 20:484–496.

   https://doi.org/10.1038/nn.4495

   • PubMed
   • Google Scholar
6. 1. Canteras NS
   2. Simerly RB
   3. Swanson LW

   (1994) Organization of projections from the ventromedial nucleus of the hypothalamus: aphaseolus vulgaris-Leucoagglutinin study in the rat

   The Journal of Comparative Neurology 348:41–79.

   https://doi.org/10.1002/cne.903480103

   • Google Scholar
7. 1. Chan KY
   2. Jang MJ
   3. Yoo BB
   4. Greenbaum A
   5. Ravi N
   6. Wu WL
   7. Sánchez-Guardado L
   8. Lois C
   9. Mazmanian SK
   10. Deverman BE
   11. Gradinaru V

   (2017) Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems

   Nature Neuroscience 20:1172–1179.

   https://doi.org/10.1038/nn.4593

   • PubMed
   • Google Scholar
8. 1. Chen SK
   2. Badea TC
   3. Hattar S

   (2011) Photoentrainment and pupillary light reflex are mediated by distinct populations of ipRGCs

   Nature 476:92–95.

   https://doi.org/10.1038/nature10206

   • PubMed
   • Google Scholar
9. 1. Ciabatti E
   2. González-Rueda A
   3. Mariotti L
   4. Morgese F
   5. Tripodi M

   (2017) Life-Long genetic and functional access to neural circuits using Self-Inactivating Rabies virus

   Cell 170:382–392.

   https://doi.org/10.1016/j.cell.2017.06.014

   • PubMed
   • Google Scholar
10. 1. Daigle TL
    2. Madisen L
    3. Hage TA
    4. Valley MT
    5. Knoblich U
    6. Larsen RS
    7. Takeno MM
    8. Huang L
    9. Gu H
    10. Larsen R
    11. Mills M
    12. Bosma-Moody A
    13. Siverts LA
    14. Walker M
    15. Graybuck LT
    16. Yao Z
    17. Fong O
    18. Nguyen TN
    19. Garren E
    20. Lenz GH
    21. Chavarha M
    22. Pendergraft J
    23. Harrington J
    24. Hirokawa KE
    25. Harris JA
    26. Nicovich PR
    27. McGraw MJ
    28. Ollerenshaw DR
    29. Smith KA
    30. Baker CA
    31. Ting JT
    32. Sunkin SM
    33. Lecoq J
    34. Lin MZ
    35. Boyden ES
    36. Murphy GJ
    37. da Costa NM
    38. Waters J
    39. Li L
    40. Tasic B
    41. Zeng H

    (2018) A suite of transgenic driver and reporter mouse lines with enhanced Brain-Cell-Type targeting and functionality

    Cell 174:465–480.

    https://doi.org/10.1016/j.cell.2018.06.035

    • PubMed
    • Google Scholar
11. 1. Das AT
    2. Tenenbaum L
    3. Berkhout B

    (2016) Tet-On systems for Doxycycline-inducible gene expression

    Current Gene Therapy 16:156–167.

    https://doi.org/10.2174/1566523216666160524144041

    • PubMed
    • Google Scholar
12. 1. Dhillon H
    2. Zigman JM
    3. Ye C
    4. Lee CE
    5. McGovern RA
    6. Tang V
    7. Kenny CD
    8. Christiansen LM
    9. White RD
    10. Edelstein EA
    11. Coppari R
    12. Balthasar N
    13. Cowley MA
    14. Chua S
    15. Elmquist JK
    16. Lowell BB

    (2006) Leptin directly activates SF1 neurons in the VMH, and this action by leptin is required for normal Body-Weight homeostasis

    Neuron 49:191–203.

    https://doi.org/10.1016/j.neuron.2005.12.021

    • Google Scholar
13. 1. Fenno LE
    2. Mattis J
    3. Ramakrishnan C
    4. Hyun M
    5. Lee SY
    6. He M
    7. Tucciarone J
    8. Selimbeyoglu A
    9. Berndt A
    10. Grosenick L
    11. Zalocusky KA
    12. Bernstein H
    13. Swanson H
    14. Perry C
    15. Diester I
    16. Boyce FM
    17. Bass CE
    18. Neve R
    19. Huang ZJ
    20. Deisseroth K

    (2014) Targeting cells with single vectors using multiple-feature boolean logic

    Nature Methods 11:763–772.

    https://doi.org/10.1038/nmeth.2996

    • PubMed
    • Google Scholar
14. 1. Fenno LE
    2. Mattis J
    3. Ramakrishnan C
    4. Deisseroth K

    (2017) A guide to creating and testing new INTRSECT constructs

    Current Protocols in Neuroscience 80:30.

    https://doi.org/10.1002/cpns.30

    • Google Scholar
15. 1. Fenno LE
    2. Ramakrishnan C
    3. Kim YS
    4. Evans KE
    5. Lo M
    6. Vesuna S
    7. Inoue M
    8. Cheung KYM
    9. Yuen E
    10. Pichamoorthy N
    11. Hong ASO
    12. Deisseroth K

    (2020) Comprehensive dual- and Triple-Feature intersectional Single-Vector delivery of diverse functional payloads to cells of behaving mammals

    Neuron 107:836–853.

    https://doi.org/10.1016/j.neuron.2020.06.003

    • PubMed
    • Google Scholar
16. 1. Gonçalves GH
    2. Li W
    3. Garcia AV
    4. Figueiredo MS
    5. Bjørbæk C

    (2014) Hypothalamic agouti-related peptide neurons and the central melanocortin system are crucial mediators of leptin's antidiabetic actions

    Cell Reports 7:1093–1103.

    https://doi.org/10.1016/j.celrep.2014.04.010

    • PubMed
    • Google Scholar
17. 1. He M
    2. Tucciarone J
    3. Lee S
    4. Nigro MJ
    5. Kim Y
    6. Levine JM
    7. Kelly SM
    8. Krugikov I
    9. Wu P
    10. Chen Y
    11. Gong L
    12. Hou Y
    13. Osten P
    14. Rudy B
    15. Huang ZJ

    (2016) Strategies and tools for combinatorial targeting of GABAergic neurons in mouse cerebral cortex

    Neuron 91:1228–1243.

    https://doi.org/10.1016/j.neuron.2016.08.021

    • PubMed
    • Google Scholar
18. 1. Hirrlinger J
    2. Scheller A
    3. Hirrlinger PG
    4. Kellert B
    5. Tang W
    6. Wehr MC
    7. Goebbels S
    8. Reichenbach A
    9. Sprengel R
    10. Rossner MJ
    11. Kirchhoff F

    (2009) Split-cre complementation indicates coincident activity of different genes in vivo

    PLOS ONE 4:e4286.

    https://doi.org/10.1371/journal.pone.0004286

    • PubMed
    • Google Scholar
19. 1. Hodge RD
    2. Bakken TE
    3. Miller JA
    4. Smith KA
    5. Barkan ER
    6. Graybuck LT
    7. Close JL
    8. Long B
    9. Johansen N
    10. Penn O
    11. Yao Z
    12. Eggermont J
    13. Höllt T
    14. Levi BP
    15. Shehata SI
    16. Aevermann B
    17. Beller A
    18. Bertagnolli D
    19. Brouner K
    20. Casper T
    21. Cobbs C
    22. Dalley R
    23. Dee N
    24. Ding SL
    25. Ellenbogen RG
    26. Fong O
    27. Garren E
    28. Goldy J
    29. Gwinn RP
    30. Hirschstein D
    31. Keene CD
    32. Keshk M
    33. Ko AL
    34. Lathia K
    35. Mahfouz A
    36. Maltzer Z
    37. McGraw M
    38. Nguyen TN
    39. Nyhus J
    40. Ojemann JG
    41. Oldre A
    42. Parry S
    43. Reynolds S
    44. Rimorin C
    45. Shapovalova NV
    46. Somasundaram S
    47. Szafer A
    48. Thomsen ER
    49. Tieu M
    50. Quon G
    51. Scheuermann RH
    52. Yuste R
    53. Sunkin SM
    54. Lelieveldt B
    55. Feng D
    56. Ng L
    57. Bernard A
    58. Hawrylycz M
    59. Phillips JW
    60. Tasic B
    61. Zeng H
    62. Jones AR
    63. Koch C
    64. Lein ES

    (2019) Conserved cell types with divergent features in human versus mouse cortex

    Nature 573:61–68.

    https://doi.org/10.1038/s41586-019-1506-7

    • PubMed
    • Google Scholar
20. 1. Krashes MJ
    2. Shah BP
    3. Madara JC
    4. Olson DP
    5. Strochlic DE
    6. Garfield AS
    7. Vong L
    8. Pei H
    9. Watabe-Uchida M
    10. Uchida N
    11. Liberles SD
    12. Lowell BB

    (2014) An excitatory paraventricular nucleus to AgRP neuron circuit that drives hunger

    Nature 507:238–242.

    https://doi.org/10.1038/nature12956

    • PubMed
    • Google Scholar
21. 1. Leshan RL
    2. Björnholm M
    3. Münzberg H
    4. Myers MG

    (2006) Leptin receptor signaling and action in the central nervous system

    Obesity 14:208S–212S.

    https://doi.org/10.1038/oby.2006.310

    • Google Scholar
22. 1. Luan H
    2. White BH

    (2007) Combinatorial methods for refined neuronal gene targeting

    Current Opinion in Neurobiology 17:572–580.

    https://doi.org/10.1016/j.conb.2007.10.001

    • PubMed
    • Google Scholar
23. 1. Meek TH
    2. Nelson JT
    3. Matsen ME
    4. Dorfman MD
    5. Guyenet SJ
    6. Damian V
    7. Allison MB
    8. Scarlett JM
    9. Nguyen HT
    10. Thaler JP
    11. Olson DP
    12. Myers MG
    13. Schwartz MW
    14. Morton GJ

    (2016) Functional identification of a neurocircuit regulating blood glucose

    PNAS 113:E2073–E2082.

    https://doi.org/10.1073/pnas.1521160113

    • PubMed
    • Google Scholar
24. 1. Mickelsen LE
    2. Bolisetty M
    3. Chimileski BR
    4. Fujita A
    5. Beltrami EJ
    6. Costanzo JT
    7. Naparstek JR
    8. Robson P
    9. Jackson AC

    (2019) Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons

    Nature Neuroscience 22:642–656.

    https://doi.org/10.1038/s41593-019-0349-8

    • PubMed
    • Google Scholar
25. 1. Patterson CM
    2. Leshan RL
    3. Jones JC
    4. Myers MG

    (2011) Molecular mapping of mouse brain regions innervated by leptin receptor-expressing cells

    Brain Research 1378:18–28.

    https://doi.org/10.1016/j.brainres.2011.01.010

    • PubMed
    • Google Scholar
26. 1. Pei Y
    2. Rogan SC
    3. Yan F
    4. Roth BL

    (2008) Engineered GPCRs as tools to modulate signal transduction

    Physiology 23:313–321.

    https://doi.org/10.1152/physiol.00025.2008

    • PubMed
    • Google Scholar
27. 1. Sciolino NR
    2. Plummer NW
    3. Chen YW
    4. Alexander GM
    5. Robertson SD
    6. Dudek SM
    7. McElligott ZA
    8. Jensen P

    (2016) Recombinase-Dependent mouse lines for chemogenetic activation of genetically defined cell Types

    Cell Reports 15:2563–2573.

    https://doi.org/10.1016/j.celrep.2016.05.034

    • PubMed
    • Google Scholar
28. 1. Senn SS
    2. Le Foll C
    3. Whiting L
    4. Tarasco E
    5. Duffy S
    6. Lutz TA
    7. Boyle CN

    (2019) Unsilencing of native LepRs in hypothalamic SF1 neurons does not rescue obese phenotype in LepR-deficient mice

    American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 317:R451–R460.

    https://doi.org/10.1152/ajpregu.00111.2019

    • PubMed
    • Google Scholar
29. 1. Vardy E
    2. Robinson JE
    3. Li C
    4. Olsen RHJ
    5. DiBerto JF
    6. Giguere PM
    7. Sassano FM
    8. Huang XP
    9. Zhu H
    10. Urban DJ
    11. White KL
    12. Rittiner JE
    13. Crowley NA
    14. Pleil KE
    15. Mazzone CM
    16. Mosier PD
    17. Song J
    18. Kash TL
    19. Malanga CJ
    20. Krashes MJ
    21. Roth BL

    (2015) A new DREADD facilitates the multiplexed chemogenetic interrogation of behavior

    Neuron 86:936–946.

    https://doi.org/10.1016/j.neuron.2015.03.065

    • PubMed
    • Google Scholar
30. 1. Vong L
    2. Ye C
    3. Yang Z
    4. Choi B
    5. Chua S
    6. Lowell BB

    (2011) Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons

    Neuron 71:142–154.

    https://doi.org/10.1016/j.neuron.2011.05.028

    • PubMed
    • Google Scholar
31. 1. Wu Z
    2. Yang H
    3. Colosi P

    (2010) Effect of genome size on AAV vector packaging

    Molecular Therapy 18:80–86.

    https://doi.org/10.1038/mt.2009.255

    • PubMed
    • Google Scholar
32. 1. Xu J
    2. Bartolome CL
    3. Low CS
    4. Yi X
    5. Chien CH
    6. Wang P
    7. Kong D

    (2018) Genetic identification of leptin neural circuits in energy and glucose homeostases

    Nature 556:505–509.

    https://doi.org/10.1038/s41586-018-0049-7

    • PubMed
    • Google Scholar
33. 1. Yang Y
    2. Wang L
    3. Bell P
    4. McMenamin D
    5. He Z
    6. White J
    7. Yu H
    8. Xu C
    9. Morizono H
    10. Musunuru K
    11. Batshaw ML
    12. Wilson JM

    (2016) A dual AAV system enables the Cas9-mediated correction of a metabolic liver disease in newborn mice

    Nature Biotechnology 34:334–338.

    https://doi.org/10.1038/nbt.3469

    • PubMed
    • Google Scholar
34. 1. Zhang J
    2. Chen D
    3. Sweeney P
    4. Yang Y

    (2020) An excitatory ventromedial hypothalamus to Paraventricular thalamus circuit that suppresses food intake

    Nature Communications 11:6326.

    https://doi.org/10.1038/s41467-020-20093-4

    • PubMed
    • Google Scholar
35. 1. Zhu Z
    2. Ma B
    3. Homer RJ
    4. Zheng T
    5. Elias JA

    (2001) Use of the Tetracycline-controlled transcriptional silencer (tTS) to eliminate transgene leak in inducible overexpression transgenic mice

    Journal of Biological Chemistry 276:25222–25229.

    https://doi.org/10.1074/jbc.M101512200

    • Google Scholar

Author details

1. Paul V Sabatini

   Department of Internal Medicine, University of Michigan, Ann Arbor, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6613-566X

2. Jine Wang

   1. Department of Internal Medicine, University of Michigan, Ann Arbor, United States
   2. Chinese academy, College of Medical Science, China Three Gorges University, Yichang, China

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Alan C Rupp

   Department of Internal Medicine, University of Michigan, Ann Arbor, United States

   Contribution

   Resources, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5363-4494

4. Alison H Affinati

   Department of Internal Medicine, University of Michigan, Ann Arbor, United States

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Jonathan N Flak

   Indiana Biosciences Research Institute, Indianapolis, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Chien Li

   Novo Nordisk Research Center, Seattle, United States

   Contribution

   Conceptualization, Writing - review and editing

   Competing interests

   CL is an employee of Novo Nordisk A/S.

7. David P Olson

   1. Department of Pediatrics, University of Michigan, Ann Arbor, United States
   2. Department of Molecular and Integrative Physiology, Ann Arbor, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

8. Martin G Myers

   1. Department of Internal Medicine, University of Michigan, Ann Arbor, United States
   2. Department of Molecular and Integrative Physiology, Ann Arbor, United States

   Contribution

   Conceptualization, Funding acquisition, Writing - original draft, Writing - review and editing

   For correspondence

   mgmyers@umich.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9468-2046

• 4,463

  views

• 573

  downloads

• 20

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.